#location data scraping

[kawaiiwizardtale]

Powerful location data scraping

Access insights on places data and gather information about various locations with our advanced web scraping solutions. Read more https://www.scrape.works/location-data

[locationscloud]

Harnessing Location Data for Effective Site Planning

In today's data-driven world, the power of location data cannot be overstated, especially in site planning and space optimization. Harnessing location data has transformed traditional site planning approaches, enabling businesses and organizations to make informed decisions that lead to maximized space usage, increased operational efficiency, and enhanced user experiences.

Location data is derived from various sources, including GPS satellites, Wi-Fi networks, and cellular towers. With the advent of smartphones and IoT devices, collecting and harnessing location data has become more accessible and accurate than ever before. This data provides insights into foot traffic patterns, dwell times, and movement trends, all of which are invaluable for site planners.

One key application of location data in site planning is retail optimization. Retailers can analyze foot traffic within their stores to identify high-traffic areas and dead zones. By understanding how customers move through a space, retailers can strategically place products, promotions, and signage to increase visibility and engagement. For instance, by identifying popular pathways, retailers can ensure that high-demand products are placed along these routes, increasing the likelihood of sales.

Location data also plays a crucial role in urban development and smart city initiatives. Planners can analyze commuting patterns, public transportation usage, and pedestrian traffic to design efficient transportation networks and infrastructure. By understanding how people move within a city, planners can create well-connected public spaces, optimize road systems, and improve overall urban accessibility.

[webscraping82]

Think Google Maps is just for directions? Think again. Businesses are turning pins into powerful insights from competitor tracking to lead generation. 👉 Read the article to know more: https://shorturl.at/20KM8

#GoogleMapsData #WebScraping #LocationIntelligence #DataDriven #PromptCloud

[3idatascraping]

A complete guide on extracting restaurant data from Google Maps includes using web scraping tools like BeautifulSoup or Scrapy in Python, leveraging the Google Places API for structured data access, and ensuring compliance with Google's terms of service. It covers steps from setup to data extraction and storage.

[iwebscrapingblogs]

Unveiling the Gastronomic Universe: Harnessing the Power of Restaurant Menu and Location Data Scraping

In the digital age, data is the new currency, and in the culinary world, restaurant menus and locations are treasure troves of information waiting to be explored. With the rise of food delivery apps, online reviews, and culinary tourism, the demand for comprehensive restaurant data has never been higher. Fortunately, the solution lies in the art of web scraping – a technique that allows us to extract valuable data from websites efficiently. In this blog post, we delve into the world of restaurant menu and location data scraping, exploring its benefits, challenges, and applications.

Understanding Restaurant Data Scraping

Restaurant data scraping involves extracting structured data from restaurant websites, including menus, locations, contact information, and more. This process typically utilizes web scraping tools and techniques to navigate through web pages, locate relevant data, and extract it in a usable format.

Unveiling the Menu: Extracting Culinary Delights

One of the primary objectives of restaurant data scraping is to extract menu information. Menus are not just lists of dishes; they represent a culinary narrative, showcasing a restaurant's identity, specialties, and culinary creativity. By scraping menu data from restaurant websites, businesses can gain insights into popular dishes, pricing strategies, ingredient trends, and menu innovations.

Pinpointing Locations: Mapping Culinary Landscapes

Location data scraping focuses on extracting information about a restaurant's physical location, including addresses, contact details, opening hours, and geographical coordinates. This data is invaluable for mapping culinary landscapes, identifying food trends across different neighborhoods, and optimizing delivery routes for food delivery services.

The Benefits of Restaurant Data Scraping

Market Research: By analyzing menu data from various restaurants, businesses can identify emerging culinary trends, consumer preferences, and competitive landscapes. This information is invaluable for market research and strategic decision-making.

Personalized Recommendations: Restaurant data scraping enables personalized recommendations for users based on their culinary preferences, dietary restrictions, and location. This enhances the user experience and increases customer satisfaction.

Operational Efficiency: For food delivery services and restaurant aggregators, location data scraping streamlines operations by providing accurate information about restaurant locations, opening hours, and delivery zones. This optimizes delivery logistics and enhances service reliability.

Competitive Intelligence: By monitoring competitor menus and pricing strategies, businesses can gain valuable insights into market dynamics, identify gaps in their offerings, and refine their marketing strategies to stay ahead of the competition.

Challenges and Considerations

While restaurant data scraping offers numerous benefits, it also presents several challenges and considerations:

Website Structure: Restaurant websites vary in structure and design, making it challenging to develop universal scraping algorithms. Customized scraping scripts may be required for each website, increasing development time and complexity.

Data Accuracy: Ensuring the accuracy and reliability of scraped data is crucial, as inaccuracies can lead to misleading insights and operational inefficiencies. Regular data validation and verification processes are essential to maintain data quality.

Ethical and Legal Considerations: Scraping data from websites without permission may raise ethical and legal concerns, particularly regarding copyright infringement and data privacy. Businesses must ensure compliance with relevant regulations and obtain consent where necessary.

Conclusion

In the era of big data, restaurant data scraping emerges as a powerful tool for unlocking valuable insights from the culinary world. By harnessing the power of menu and location data scraping, businesses can gain a competitive edge, enhance customer experiences, and navigate the ever-evolving landscape of the food industry. However, it is essential to approach data scraping ethically, respecting website terms of service and privacy policies, to ensure a sustainable and responsible data ecosystem. As technology continues to evolve, the possibilities for restaurant data scraping are limitless, promising to revolutionize the way we explore, experience, and appreciate the culinary universe.

[lensnure]

E-commerce data scraping provides detailed information on market dynamics, prevailing patterns, pricing data, competitors’ practices, and challenges.

Scrape E-commerce data such as products, pricing, deals and offers, customer reviews, ratings, text, links, seller details, images, and more. Avail of the E-commerce data from any dynamic website and get an edge in the competitive market. Boost Your Business Growth, increase revenue, and improve your efficiency with Lensnure's custom e-commerce web scraping services.

We have a team of highly qualified and experienced professionals in web data scraping.

[anaquariusfox]

I spent the evening looking into this AI shit and made a wee informative post of the information I found and thought all artists would be interested and maybe help yall?

edit: forgot to mention Glaze and Nightshade to alter/disrupt AI from taking your work into their machines. You can use these and post and it will apparently mess up the AI and it wont take your content into it's machine!

edit: ArtStation is not AI free! So make sure to read that when signing up if you do! (this post is also on twt)

[Image descriptions: A series of infographics titled: “Opt Out AI: [Social Media] and what I found.” The title image shows a drawing of a person holding up a stack of papers where the first says, ‘Terms of Service’ and the rest have logos for various social media sites and are falling onto the floor. Long transcriptions follow.

Instagram/Meta (I have to assume Facebook).

Hard for all users to locate the “opt out” options. The option has been known to move locations.

You have to click the opt out link to submit a request to opt out of the AI scraping. *You have to submit screenshots of your work/face/content you posted to the app, is curretnly being used in AI. If you do not have this, they will deny you.

Users are saying after being rejected, are being “meta blocked”

People’s requests are being accepted but they still have doubts that their content won’t be taken anyways.

Twitter/X

As of August 2023, Twitter’s ToS update:

“Twitter has the right to use any content that users post on its platform to train its AI models, and that users grant Twitter a worldwide, non-exclusive, royalty-free license to do so.”

There isn’t much to say. They’re doing the same thing Instagram is doing (to my understanding) and we can’t even opt out.

Tumblr

They also take your data and content and sell it to AI models.

But you’re in luck!

It is very simply to opt out (Wow. Thank Gods)

Opt out on Desktop: click on your blog > blog settings > scroll til you see visibility options and it’ll be the last option to toggle

Out out of Mobile: click your blog > scroll then click visibility > toggle opt out option

TikTok

I took time skim their ToS and under “How We Use Your Information” and towards the end of the long list: “To train and improve our technology, such as our machine learning models and algorithms.”

Regarding data collected; they will only not sell your data when “where restricted by applicable law”. That is not many countries. You can refuse/disable some cookies by going into settings > ads > turn off targeted ads.

I couldn’t find much in AI besides “our machine learning models” which I think is the same thing.

What to do?

In this age of the internet, it’s scary! But you have options and can pick which are best for you!

Accepting these platforms collection of not only your artwork, but your face! And not only your faces but the faces of those in your photos. Your friends and family. Some of those family members are children! Some of those faces are minors! I shudder to think what darker purposes those faces could be used for.

Opt out where you can! Be mindful and know the content you are posting is at risk of being loaded to AI if unable to opt out.

Fully delete (not archive) your content/accounts with these platforms. I know it takes up to 90 days for instagram to “delete” your information. And even keep it for “legal” purposes like legal prevention.

Use lesser known social media platforms! Some examples are; Signal, Mastodon, Diaspora, et. As well as art platforms: Artfol, Cara, ArtStation, etc.

The last drawing shows the same person as the title saying, ‘I am, by no means, a ToS autistic! So feel free to share any relatable information to these topics via reply or qrt!

I just wanted to share the information I found while searching for my own answers cause I’m sure people have the same questions as me.’ \End description] (thank you @a-captions-blog!)

[actosoluions]

How to Scrape Tweets Data by Location Using Python and snscrape?

In this blog, we will take a comprehensive look into scraping Python wrapper and its functionality and specifically focus on using it to search for tweets based on location. We will also delve into why the wrapper may not always perform as expected. Let's dive in

snscrape is a remarkable Python library that enables users to scrape tweets from Twitter without the need for personal API keys. With its lightning-fast performance, it can retrieve thousands of tweets within seconds. Moreover, snscrape offers powerful search capabilities, allowing for highly customizable queries. While the documentation for scraping tweets by location is currently limited, this blog aims to comprehensively introduce this topic. Let's delve into the details:

Introduction to Snscrape: Snscrape is a feature-rich Python library that simplifies scraping tweets from Twitter. Unlike traditional methods that require API keys, snscrape bypasses this requirement, making it accessible to users without prior authorization. Its speed and efficiency make it an ideal choice for various applications, from research and analysis to data collection.

The Power of Location-Based Tweet Scraping: Location-based tweet scraping allows users to filter tweets based on geographical coordinates or place names. This functionality is handy for conducting location-specific analyses, monitoring regional trends, or extracting data relevant to specific areas. By leveraging Snscrape's capabilities, users can gain valuable insights from tweets originating in their desired locations.

Exploring Snscrape's Location-Based Search Tools: Snscrape provides several powerful tools for conducting location-based tweet searches. Users can effectively narrow their search results to tweets from a particular location by utilizing specific parameters and syntax. This includes defining the search query, specifying the geographical coordinates or place names, setting search limits, and configuring the desired output format. Understanding and correctly using these tools is crucial for successful location-based tweet scraping.

Overcoming Documentation Gaps: While snscrape is a powerful library, its documentation on scraping tweets by location is currently limited. This article will provide a comprehensive introduction to the topic to bridge this gap, covering the necessary syntax, parameters, and strategies for effective location-based searches. Following the step-by-step guidelines, users can overcome the lack of documentation and successfully utilize snscrape for their location-specific scraping needs.

Best Practices and Tips: Alongside exploring Snscrape's location-based scraping capabilities, this article will also offer best practices and tips for maximizing the efficiency and reliability of your scraping tasks. This includes handling rate limits, implementing error-handling mechanisms, ensuring data consistency, and staying updated with any changes or updates in Snscrape's functionality.

Introduction of snscrape Using Python

In this blog, we’ll use tahe development version of snscrape that can be installed withpip install git+https://github.com/JustAnotherArchivist/snscrape.git

Note: this needs Python 3.8 or latest

Some familiarity of the Pandas module is needed.

We encourage you to explore and experiment with the various features of snscrape to better understand its capabilities. Additionally, you can refer to the mentioned article for more in-depth information on the subject. Later in this blog, we will delve deeper into the user field and its significance in tweet scraping. By gaining a deeper understanding of these concepts, you can harness the full potential of snscrape for your scraping tasks.

Advanced Search Features

In this code snippet, we define the search query as "pizza near:Los Angeles within:10km", which specifies that we want to search for tweets containing the word "pizza" near Los Angeles within a radius of 10 km. The TwitterSearchScraper object is created with the search query, and then we iterate over the retrieved tweets and print their content.

Feel free to adjust the search query and radius per your specific requirements.

For comparing results, we can utilize an inner merging on two DataFrames:common_rows = df_coord.merge(df_city, how='inner')

That returns 50 , for example, they both have the same rows.

What precisely is this place or location?

When determining the location of tweets on Twitter, there are two primary sources: the geo-tag associated with a specific tweet and the user's location mentioned in their profile. However, it's important to note that only a small percentage of tweets (approximately 1-2%) are geo-tagged, making it an unreliable metric for location-based searches. On the other hand, many users include a location in their profile, but it's worth noting that these locations can be arbitrary and inaccurate. Some users provide helpful information like "London, England," while others might use humorous or irrelevant descriptions like "My Parents' Basement."

Despite the limited availability and potential inaccuracies of geo-tagged tweets and user profile locations, Twitter employs algorithms as part of its advanced search functionality to interpret a user's location based on their profile. This means that when you look for tweets through coordinates or city names, the search results will include tweets geotagged from the location and tweets posted by users who have that location (or a location nearby) mentioned in their profile.

To illustrate the usage of location-based searching on Twitter, let's consider an example. Suppose we perform a search for tweets near "London." Here are two examples of tweets that were found using different methods:

The first tweet is geo-tagged, which means it contains specific geographic coordinates indicating its location. In this case, the tweet was found because of its geo-tag, regardless of whether the user has a location mentioned in their profile or not.

The following tweet isn’t geo-tagged, which means that it doesn't have explicit geographic coordinates associated with it. However, it was still included in the search results because a user has given a location in the profile that matches or is closely associated with London.

When performing a location-based search on Twitter, you can come across tweets that are either geo-tagged or have users with matching or relevant locations mentioned in their profiles. This allows for a more comprehensive search, capturing tweets from specific geographic locations and users who have declared their association with those locations.

Get Location From Scraped Tweets

If you're using snscrape to scrape tweets and want to extract the user's location from the scraped data, you can do so by following these steps. In the example below, we scrape 50 tweets within a 10km radius of Los Angeles, store the data in a DataFrame, and then create a new column to capture the user's location.

If It Doesn’t Work According to Your Expectations

The use of the near: and geocode: tags in Twitter's advanced search can sometimes yield inconsistent results, especially when searching for specific towns, villages, or countries. For instance, while searching for tweets nearby Lewisham, the results may show tweets from a completely different location, such as Hobart, Australia, which is over 17,000 km away.

To ensure more accurate results when scraping tweets by locations using snscrape, it is recommended to use the geocode tag having longitude & latitude coordinates, along with a specified radius, to narrow down the search area. This approach will provide more reliable and precise results based on the available data and features.

Conclusion

In conclusion, the snscrape Python module is a valuable tool for conducting specific and powerful searches on Twitter. Twitter has made significant efforts to convert user input locations into real places, enabling easy searching by name or coordinates. By leveraging its capabilities, users can extract relevant information from tweets based on various criteria.

For research, analysis, or other purposes, snscrape empowers users to extract valuable insights from Twitter data. Tweets serve as a valuable source of information. When combined with the capabilities of snscrape, even individuals with limited experience in Data Science or subject knowledge can undertake exciting projects.

Happy scrapping!

For more details, you can contact Actowiz Solutions anytime! Call us for all your mobile app scraping and web scraping services requirements.

sources :https://www.actowizsolutions.com/how-to-scrape-tweets-data-by-location-using-python-and-snscrape.php

[zenithsturniolo]

SCREAM, BITCH - ghostface!chris x blogger!reader

♬ . ݁₊ ⊹ . ݁˖ . ݁ series intro | 1 | 2 | 3 | 4

chapter four: what have you done?

this chapter will contain.. murder, graphic violence, home invasion, blood, stabbing, strangulation, psychological horror, stalking, obsessive behavior, character death, mistaken identity, emotional shock, and strong language. wc: 2.1k series summary: a dark, twisted slowburn where obsession bleeds into desire. you're a true crime blogger. he's the masked stranger recreating your cases. dual povs, filthy tension, and cliffhangers sharp enough to scar. it’s not just stalking - it’s seduction. not just fear - it’s fascination. you wanted a story. he wanted you. now you’re both in far too deep.

♯ chris pov

I saw you in the car with someone else and couldn't sleep If somethin' happens to him, you can bet that it was me

it takes every ounce of chris’s self-control not to march into that cafe and drill liam’s skull into the checkered tile with a 9mm.

he’s parked just across the street, tucked behind the smudged glass of matt’s car, engine off, breath fogging up the windshield in slow, shaky clouds. his jaw pulses with tension. his grip on the steering wheel is iron-tight, veins raised under pale skin, silver rings glinting ominously in the fading light.

the cafe sits at the edge of town — a washed-out, run-down joint with chipped red bricks and a crooked neon sign that flickers weakly in the dusky air. he only knows about it because he dug deep. combed through tagged locations on your socials. cross-checked timestamps, even scraped data off old texts. turns out this is your spot. yours and liam’s.

the knowledge makes his teeth grind. the rage simmers behind his eyes like lava.

his stare cuts through the thick glass windows and lands on you like a fucking missile. you’re seated at the far table, light pouring over your skin, illuminating every perfect inch of you like you’re in some goddamn film. your laughter breaks through the air like bells — easy, rich, unbothered. you look alive. completely unguarded. you’re glowing, christ, and you don’t even know how dangerous that is.

you toss your head back at something liam says, your shoulders shaking with amusement, fingers tapping against the chipped tabletop. he’s leaning in, of course he is — too close, eyes fixed on you with a stupid grin plastered across his face. his arm grazes yours when he moves. you don’t flinch. you laugh.

chris swears the sound is loud enough to puncture his eardrums.

to anyone else, it might look like a casual hangout — two friends catching up, maybe something sweet in the air.

but chris knows better. he always knows better.

it’s not paranoia. it’s instinct. it’s being a guy and knowing how guys look at things they want to own.

liam is fucking starving for you. it’s all over him. the subtle lean, the way his pupils follow your hands, your lips, the sway of your body when you shift. how he soaks in your laugh like a man dying of thirst. how he doesn’t blink when you touch him.

he likes you so fucking bad. and the worst part?

you don’t even see it.

but chris sees it. he sees everything.

he leans back in the seat, head thudding against the cracked leather headrest, a long breath wheezing out of his flared nostrils. his heart pounds so loud it fills the car like bass.

he’s bloodshot and brittle, dark circles hanging like bruises under his eyes. a hoodie’s pulled low over his head, hat jammed on top, messy tufts of hair sticking out. the Fresh Love logo on his chest is half-faded, the sweater stained with soda and sweat. his fingers — long, delicate, stained with old ink — twist his sunglasses between them so hard they creak under the pressure. his jaw ticks. a nerve jumps in his temple.

and beneath all that simmering fury — he’s jealous.

ugly, rabid jealousy, the kind that coils in his gut and sharpens his teeth.

your body is doubled over the table now, wheezing with laughter, clutching at liam’s shoulder like you can’t breathe without him. chris stares with an empty sort of hunger, so consumed by loathing and longing that it makes him dizzy. he feels like he’s about to combust in the driver’s seat.

he wants to crack open the car door, cross the street, and drag you away by the wrist.

you’re his.

you’re his.

you’re his and only his.

that’s the mantra that threads through his brain on repeat, dulling the edge of his fury as he clings to the steering wheel. you’ll see eventually. you’ll understand the depth of his love — how it grows wild like ivy, how it would kill for you, die for you, do anything for you.

you’ll love him back. you will. you have to.

first, he just has to finish the plan. ride the wave. keep it together long enough to reach the end.

his phone buzzes in the cupholder, vibrating loud against the silence. he glances down:

Matt: yo? wya?? dude

Nick: chris hellooo?? where are you??

he groans, rubbing his temples until stars spark behind his lids.

first, he’ll go home and get a pepsi for the pounding between his ears. then he’ll film the stupid youtube video. maybe buy his own car.

and then, he’ll become ghostface.

— 

chris steps through the front door, movements heavy. the house is dipped in late-evening gold, sunlight spilling across the hardwood in warm slashes. it smells faintly of cinnamon and laundry detergent — familiar. safe.

it makes the guilt in his stomach burn hotter.

“‘m home,” he mutters, voice raspy from silence and restraint.

he kicks off his shoes, shoulders tight, heading to the kitchen. the keys land on the counter with a clatter. nick’s seated at the dining table, laptop aglow as he scrolls through analytics for his lip balm line. his jaw’s locked, eyes following chris like a warning.

matt leans against the fridge, gum chewing halted, strands of hair falling into his eyes. the tension is thick — immediate and suffocating.

“where were you?” nick asks slowly. cautious. like he’s approaching an animal that might bite.

“out.” chris pulls his hood off, running a shaky hand through his hair. his scalp burns under the touch. he doesn’t meet their eyes.

matt shifts. “you’ve been gone a lot, man. you don’t sleep anymore. you barely eat.”

“busy.” chris opens the fridge with one hand and snags a can of pepsi. “meetings. brand shit.”

nick clicks his tongue. “we haven’t had a meeting in days.”

“jesus fucking christ,” chris snaps, the can buckling slightly under his grip. “will you guys back off?”

nick flinches, mouth parting in shock. matt says nothing — just watches, eyes pinned to chris’s expression like he’s trying to read through the static.

“seriously,” chris continues, voice rising. “i’m not a kid. i don’t need a babysitter. i can breathe without telling you about it, okay? so stop blowing up my goddamn phone and get the fuck off my back.”

he storms upstairs before he can see their faces. the door slams shut behind him like a gunshot.

his hands tremble as he opens the can and chugs. the sweetness burns down his throat, fizzling in his chest. he swallows down the rage, the panic, the obsession clawing at his ribs. but it’s not enough. not even close.

because everything — everything — leads back to you.

the want, the ache, the fucking need that lives under his skin like a parasite. he can't breathe without thinking about you. he doesn't exist without you.

he drops the empty can into the trash. stares at the wall, the floor, the bed. the buzzing in his skull softens. guilt licks at his chest like a slow-burning fire. not for the things he’s done.

but for yelling. for letting the mask slip in front of the only people who’ve ever seen his real face.

funny, he thinks. he can strangle someone with a smile, but raising his voice at nick makes his stomach curl.

his door creaks open. he tenses — but it’s matt. slow steps. quiet concern.

matt sits beside him on the bed, silent. their shoulders brush.

a steady hand on chris’s back. warm. grounding. 

“do what you gotta do,” matt says softly. “you don’t have to talk. but we’re here. you hear me?”

chris nods into his brother’s shoulder.

it’s the only safe place he knows — besides you.

—

the street is almost too quiet. one of those thick, breathless nights where the sky hangs heavy with unshed rain and the air tastes metallic, like something bad is about to happen. shadows pool under the streetlights, and the clouds don’t move — they just hover there, bloated and waiting.

chris doesn’t mind the stillness. he sits behind the wheel, parked far enough down the block that her porch light doesn’t reach him, the engine long since killed, the heat of the day fading from the hood. it’s dark. it’s perfect.

inside the little bungalow, the girl — morgan — moves around like she’s safe. like she hasn’t been followed for the last five days. like she didn’t just walk straight into the blueprint he built around her. she’s in the living room, maybe fifteen feet away, clad in fuzzy socks and drowning in a hoodie that reaches her thighs.

the scent of buttery popcorn slips out through the half-cracked kitchen window, and somewhere deeper in the house, a record spins something soft and jazzy. the curtains are mostly drawn, but he can see enough — the lazy arch of her back as she stretches, the way her hair’s tied up loose and messy like she didn’t expect anyone to see her tonight.

the hoodie catches his eye. navy blue. old. the kind of fabric that pills after too many washes. there’s a logo stretched across the chest, half-faded, but he knows it. something flickers in the back of his mind — a cold crawl down his spine — but he doesn’t grab it. doesn’t pull on the thread. not yet. he inhales slowly, deeply, holds the breath in his chest and watches.

the mask waits in the passenger seat, glossy and empty. when he lifts it, his reflection warps in the surface — all smooth porcelain and hollow eyes. he doesn’t hesitate. it slides over his face like ritual.

the window screen on the side of the house is just as loose as he remembered. he nudges it free, slips it aside, pushes the glass open an inch at a time until the space is wide enough to slide through. inside, the house smells warm and lived-in — vanilla candles, laundry detergent, popcorn and shampoo.

his boots don’t make a sound. he moves like smoke through the kitchen, knowing every drawer before he opens it. the knife is heavier than he likes, thick and serrated, meant for meat. it’ll do.

she’s brushing her teeth in the bathroom, humming to herself. it’s soft, tuneless, the kind of sound people only make when they’re alone and completely unworried. he can hear the toothbrush clatter into the cup. water run. spit hit porcelain. she wipes her mouth with her sleeve, flicks the light off, and steps into the hallway without even looking up.

he sees her before she sees him — her silhouette backlit by the warm bedroom lamp, phone in one hand, thumb scrolling, still riding the tail end of whatever video or message she’d been laughing at.

and then she looks up.

for one thick second, everything stops. her whole body stutters. her breath catches halfway out. the phone slips from her fingers and hits the hardwood with a crunch of glass.

he doesn’t give her time to scream.

his hand shoots out, slamming her back into the wall so hard the frame on the hallway shelf wobbles and falls. her gasp is short and jagged, eyes wide as the knife flashes in the amber light. she fights — nails raking the mask, fingers clutching at his forearm — but he’s already braced for it. his grip is iron. 

“don’t,” he says, voice low and static-thick beneath the mask. “don’t make this harder.”

her mouth trembles like she wants to say something, but she doesn’t get the chance. his hand closes around her throat, pinning her higher against the wall, her heels dragging against the floor. the blade presses into her side. she feels it — the cold of it, the pressure — just before it pushes in. slow. steady. deliberate.

her body jerks, a half-choked scream bubbling in her throat, but it dies before it reaches her lips. the warmth spreads fast, thick and pulsing, slick between his fingers.

her legs buckle. her hands fall away from his arms. there’s a wet rattle in her chest, one final breath that doesn’t go anywhere. her eyes lock on his — wide, confused, glassy — and then they just… go out.

he lowers her gently, almost reverently, until her body slumps against the floor. he holds her there for a beat longer, studying her, head tilted. his own heartbeat stays steady. nothing rushes. this isn’t chaos. this is control.

he wipes the blade across the front of her hoodie — lazy, efficient — and tosses it onto the bed. the girl is already starting to cool. blood pools like ink, a soft red halo beneath her.

he’s halfway through resetting everything — window back in place, surfaces clean — when the buzz starts.

a faint vibration on the floor, muffled beneath shards of cracked glass. her phone, screen face-down, still blinking.

he glances at it without much thought. just muscle memory. he crouches, gloved fingers curling around the edges as he flips it over.

texts light the screen.

Matt: on my way now u want starbucks? babyyyy 

the world tilts.

he stares at the words, the shape of them, the nickname, the casualness. his stomach twists before his brain catches up.

and then, like floodlights flicking on, it hits.

chris has just murdered his brother’s girlfriend.

find parts of this series here !

a/n: let me cook 🔥

🏷: @drewswife @k4urltzx @courta13 @briizysturn @y2kstarr @chriscantwhisper @tezzzzzzzz @adorechris @dolliraez @rriverscuomo @sturnsblogs @mattspillowprincess @mattsplaything @sturns-mermaid @auttysturnz @sonnyangelsweetiee @izzylovesmatt @ribbonlovergirl @k4urltzx @matts-girlfriend @pair-of-pantaloons @444sturns @weron1ka @grrrrcherries  @matts-wife @thicknick19 @slvtf0rchr1s @devotedlyteenagemusic @adoremattsturns @slut4chrisloads @cayleeuhithinknott

divider by @anitalenia

this series is a work of fiction created for entertainment purposes only. all characters, events, and dialogue are entirely fictional and should not be interpreted as real. any similarities to real people or events are purely coincidental. credit and respect to all creators who’ve inspired similar works before me. I claim ownership only over my original writing, ideas, and interpretations. please do not repost, plagiarize, or steal. reblogs and love are always appreciated.

© zenithsturniolo

[cursedwithcaution]

Okay idk if this has already been theorized but I think I know what MDR is doing.

MDR spends all day sorting numbers based on, essentially, vibes. Helly is told that she needs to look for “scary numbers.”

Down on the testing floor, Gemma is put through rooms whose names align with MDR’s files.

Lumon is trying to create a version of severance that will allow people (probably the ultra rich) to siphon any unpleasant experience, ranging (from what we’ve seen) from dental procedures to writing thank you notes.

Gemma is asked how she feels afterwards. She tells them that her hand hurts. Not the emotion, just the physical pain that stays with her when she leaves the room.

The severance chip as we know it is locationally activated. We have the exceptions with OTC and the ORTBO (unless that wasn’t actually outside the facility), but the chips are based on location or manual control by a third party.

But we don’t always know where there will be an unpleasant experience. We can predict that the dentist will be uncomfortable or painful, but we don’t know if we’re going to, for instance, fall down and scrape a knee. But once we know something unpleasant or “bad” is about to happen, our brains react with stress, fear, etc.

I think MDR’s job is to isolate the numbers that represent Gemma’s uncomfortable (“negative”) emotions, repeating this process over and over until Lumon can use the data to activate severance chips with emotional responses rather than based on location. Because the goal, based on what we know and can guess, is for severance to be used to keep certain people from suffering, leaving a portion of themselves to experience anything they deem too mundane, painful, or upsetting.

Gemma’s brain has been severed into who knows how many innies. She’s been pushed to the limit, likely in ways worse than what we saw in episode 7.

I think MDR are sorting the numbers that represent pain/discomfort, and each room is designed based on the greatest amount of pain experienced in the other rooms, leaving Cold Harbor as the final boss of “how much can we make an innie suffer before they break?” Or something like that.

For some reason, Mark S is the only person who can finish Cold Harbor. He is unknowingly creating the worst torture specifically for Gemma. By sorting scary numbers into little boxes.

[mariacallous]

In late January, a warning spread through the London-based Facebook group Are We Dating the Same Guy?—but this post wasn’t about a bad date or a cheating ex. A connected network of male-dominated Telegram groups had surfaced, sharing and circulating nonconsensual intimate images of women. Their justification? Retaliation.

On January 23, users in the AWDTSG Facebook group began warning about hidden Telegram groups. Screenshots and TikTok videos surfaced, revealing public Telegram channels where users were sharing nonconsensual intimate images. Further investigation by WIRED identified additional channels linked to the network. By scraping thousands of messages from these groups, it became possible to analyze their content and the patterns of abuse.

AWDTSG, a sprawling web of over 150 regional forums across Facebook alone, with roughly 3 million members worldwide, was designed by Paolo Sanchez in 2022 in New York as a space for women to share warnings about predatory men. But its rapid growth made it a target. Critics argue that the format allows unverified accusations to spiral. Some men have responded with at least three defamation lawsuits filed in recent years against members, administrators, and even Meta, Facebook’s parent company. Others took a different route: organized digital harassment.

Primarily using Telegram group data made available through Telemetr.io, a Telegram analytics tool, WIRED analyzed more than 3,500 messages from a Telegram group linked to a larger misogynistic revenge network. Over 24 hours, WIRED observed users systematically tracking, doxing, and degrading women from AWDTSG, circulating nonconsensual images, phone numbers, usernames, and location data.

From January 26 to 27, the chats became a breeding ground for misogynistic, racist, sexual digital abuse of women, with women of color bearing the brunt of the targeted harassment and abuse. Thousands of users encouraged each other to share nonconsensual intimate images, often referred to as “revenge porn,” and requested and circulated women’s phone numbers, usernames, locations, and other personal identifiers.

As women from AWDTSG began infiltrating the Telegram group, at least one user grew suspicious: “These lot just tryna get back at us for exposing them.”

When women on Facebook tried to alert others of the risk of doxing and leaks of their intimate content, AWDTSG moderators removed their posts. (The group’s moderators did not respond to multiple requests for comment.) Meanwhile, men who had previously coordinated through their own Facebook groups like “Are We Dating the Same Girl” shifted their operations in late January to Telegram's more permissive environment. Their message was clear: If they can do it, so can we.

"In the eyes of some of these men, this is a necessary act of defense against a kind of hostile feminism that they believe is out to ruin their lives," says Carl Miller, cofounder of the Center for the Analysis of Social Media and host of the podcast Kill List.

The dozen Telegram groups that WIRED has identified are part of a broader digital ecosystem often referred to as the manosphere, an online network of forums, influencers, and communities that perpetuate misogynistic ideologies.

“Highly isolated online spaces start reinforcing their own worldviews, pulling further and further from the mainstream, and in doing so, legitimizing things that would be unthinkable offline,” Miller says. “Eventually, what was once unthinkable becomes the norm.”

This cycle of reinforcement plays out across multiple platforms. Facebook forums act as the first point of contact, TikTok amplifies the rhetoric in publicly available videos, and Telegram is used to enable illicit activity. The result? A self-sustaining network of harassment that thrives on digital anonymity.

TikTok amplified discussions around the Telegram groups. WIRED reviewed 12 videos in which creators, of all genders, discussed, joked about, or berated the Telegram groups. In the comments section of these videos, users shared invitation links to public and private groups and some public channels on Telegram, making them accessible to a wider audience. While TikTok was not the primary platform for harassment, discussions about the Telegram groups spread there, and in some cases users explicitly acknowledged their illegality.

TikTok tells WIRED that its Community Guidelines prohibit image-based sexual abuse, sexual harassment, and nonconsensual sexual acts, and that violations result in removals and possible account bans. They also stated that TikTok removes links directing people to content that violates its policies and that it continues to invest in Trust and Safety operations.

Intentionally or not, the algorithms powering social media platforms like Facebook can amplify misogynistic content. Hate-driven engagement fuels growth, pulling new users into these communities through viral trends, suggested content, and comment-section recruitment.

As people caught notice on Facebook and TikTok and started reporting the Telegram groups, they didn’t disappear—they simply rebranded. Reactionary groups quickly emerged, signaling that members knew they were being watched but had no intention of stopping. Inside, messages revealed a clear awareness of the risks: Users knew they were breaking the law. They just didn’t care, according to chat logs reviewed by WIRED. To absolve themselves, one user wrote, “I do not condone im [simply] here to regulate rules,” while another shared a link to a statement that said: “I am here for only entertainment purposes only and I don’t support any illegal activities.”

Meta did not respond to a request for comment.

Messages from the Telegram group WIRED analyzed show that some chats became hyper-localized, dividing London into four regions to make harassment even more targeted. Members casually sought access to other city-based groups: “Who’s got brum link?” and “Manny link tho?”—British slang referring to Birmingham and Manchester. They weren’t just looking for gossip. “Any info from west?” one user asked, while another requested, “What’s her @?”— hunting for a woman’s social media handle, a first step to tracking her online activity.

The chat logs further reveal how women were discussed as commodities. “She a freak, I’ll give her that,” one user wrote. Another added, “Beautiful. Hide her from me.” Others encouraged sharing explicit material: “Sharing is caring, don’t be greedy.”

Members also bragged about sexual exploits, using coded language to reference encounters in specific locations, and spread degrading, racial abuse, predominantly targeting Black women.

Once a woman was mentioned, her privacy was permanently compromised. Users frequently shared social media handles, which led other members to contact her—soliciting intimate images or sending disparaging texts.

Anonymity can be a protective tool for women navigating online harassment. But it can also be embraced by bad actors who use the same structures to evade accountability.

"It’s ironic," Miller says. "The very privacy structures that women use to protect themselves are being turned against them."

The rise of unmoderated spaces like the abusive Telegram groups makes it nearly impossible to trace perpetrators, exposing a systemic failure in law enforcement and regulation. Without clear jurisdiction or oversight, platforms are able to sidestep accountability.

Sophie Mortimer, manager of the UK-based Revenge Porn Helpline, warned that Telegram has become one of the biggest threats to online safety. She says that the UK charity’s reports to Telegram of nonconsensual intimate image abuse are ignored. “We would consider them to be noncompliant to our requests,” she says. Telegram, however, says it received only “about 10 piece of content” from the Revenge Porn Helpline, “all of which were removed.” Mortimer did not yet respond to WIRED’s questions about the veracity of Telegram’s claims.

Despite recent updates to the UK’s Online Safety Act, legal enforcement of online abuse remains weak. An October 2024 report from the UK-based charity The Cyber Helpline shows that cybercrime victims face significant barriers in reporting abuse, and justice for online crimes is seven times less likely than for offline crimes.

"There’s still this long-standing idea that cybercrime doesn’t have real consequences," says Charlotte Hooper, head of operations of The Cyber Helpline, which helps support victims of cybercrime. "But if you look at victim studies, cybercrime is just as—if not more—psychologically damaging than physical crime."

A Telegram spokesperson tells WIRED that its moderators use “custom AI and machine learning tools” to remove content that violates the platform's rules, “including nonconsensual pornography and doxing.”

“As a result of Telegram's proactive moderation and response to reports, moderators remove millions of pieces of harmful content each day,” the spokesperson says.

Hooper says that survivors of digital harassment often change jobs, move cities, or even retreat from public life due to the trauma of being targeted online. The systemic failure to recognize these cases as serious crimes allows perpetrators to continue operating with impunity.

Yet, as these networks grow more interwoven, social media companies have failed to adequately address gaps in moderation.

Telegram, despite its estimated 950 million monthly active users worldwide, claims it’s too small to qualify as a “Very Large Online Platform” under the European Union’s Digital Service Act, allowing it to sidestep certain regulatory scrutiny. “Telegram takes its responsibilities under the DSA seriously and is in constant communication with the European Commission,” a company spokesperson said.

In the UK, several civil society groups have expressed concern about the use of large private Telegram groups, which allow up to 200,000 members. These groups exploit a loophole by operating under the guise of “private” communication to circumvent legal requirements for removing illegal content, including nonconsensual intimate images.

Without stronger regulation, online abuse will continue to evolve, adapting to new platforms and evading scrutiny.

The digital spaces meant to safeguard privacy are now incubating its most invasive violations. These networks aren’t just growing—they’re adapting, spreading across platforms, and learning how to evade accountability.

[drabblesandimagines]

Dove

Leon Kennedy x fem reader Thinking of making this a little series, will be a fluff, bit of a slow burn, bodyguard trope?

You aren’t sure how you’d got through the last few hours.  Everything’s a blur as you try to think back of the horror that had occurred, now you’re now sat in an unfamiliar chair in an unfamiliar office. Your right arm is in a sling, shoulder throbbing somewhat from a reset dislocation, broken fingers splintered together on the same arm, medical tape holding a wound closed on your temple, disinfectant swiped across the numerous scrapes, your body aching with developing bruises on your legs, poking out from under your dress, from the fall down the stairs – the fall that apparently ended up saving your life from the unearthly creature that had rampaged through your workplace and tore your co-workers apart.

After being treated by a DSO medic, you’d been escorted by a tall, armed to the gills, annoyingly silent man. He’d confiscated your phone, despite the fact the screen was smashed and wouldn’t turn on, and taken you across the city to the main HQ, ushered up a side entrance into the room you now sat, told you to wait, and left you alone for what felt like hours.

The door eventually opens and a smartly dressed, pretty woman, hair pinned up in a bun and wearing glasses enters, immediately heading to the other side of the desk and taking what you assumed was her seat. A handsome man accompanied her, shaggy brown hair, dressed in cargo pants, fingerless gloves, knife strapped to his thigh, finished off with a leather jacket, a holster poking out from underneath. He gives you a sympathetic once over as he sits down besides you, careful not to brush your knee with his own as he does. Considerate.

“Were you given adequate pain medication?” The lady asks abruptly, beginning to type on her keyboard.

You stare at her a moment – she’s all business. “Er… Yeah. Thanks.” Though you’re sure the two of them have noticed the wince as you shuffled in your seat. The medic had offered you stronger stuff but you’d declined, wanting to keep your wits about you. “Sorry, what’s happening now?”

“I’m Ingrid Hunnigan, this is Agent Kennedy.” She nods to the man opposite her.

“Name’s Leon.” The man besides you offers his hand and you notice he’s adapted for your incapacitated arm, in what will surely result in a very awkward handshake but the gesture is nice. You take it, hoping the tremor in your grip isn’t so painfully obvious. “Hi. Erm, I’m-”

“Dove.” Hunnigan cuts you off. “I am aware of your identity, but we will be referring to you as Dove.”

“It’s a codename.” Leon explains, a little less business. “For your safety.”

Hunnigan pauses in her typing, hitting backspace slowly as she replies. “Agent Kennedy will be your protection detail until we get this mess squared up.”

Your breath catches in your throat at her choice of word, a sick feeling twisting in your stomach. “Mess? It was a massacre in there-”

“I know. We know.” The agent besides you stresses. “I’m sorry you had to see all that.”

“Am I the only one who…?” You don’t know why you ask.

“I’m afraid so.” Hunnigan replies, a little softer in tone. “We’re going to send you to a safe house. Agent Kennedy will stay with you.”

“O-okay.” You nod, not taking it all in. “You… You think they’d send whatever that thing was after me?”

“That’s what we need time to establish.” Hunnigan replies. “From the CCTV, after the attack, there was a breach on the database. We need to establish how much data they managed to extract, if any. Agent Kennedy will keep you updated as much as he can when he receives any intel.” She turns more to him then, cutting you out of the conversation. “I’ll send the co-ordinates of the safe house when you’re out of the city. They’re loading up an SUV with supplies for at least a week. If it goes on longer, we’ll arrange a supply drop via another location.”

“That long?” You feel like you’re interrupting.

“Worse case scenario, Dove.” Leon offers you a smile. “I’m sure we’ll have you back home in no time. Did they send you away with any meds?”

“The medic sent in a report – with a treatment plan. It’s in the information pack, prescribed medicine is in with the supplies. Again, enough for a week.” Hunnigan replies. “I’ve arranged clothes too – medic guessed your size for me. We’ll be keeping your phone for now.”

“Why?”

“We can’t allow you to contact anyone – for your safety and theirs.”

Your heart skips a beat at that comment. “Wait… You think I might be behind this, don’t you?”

Hunnigan purses her lips. “It is an avenue we need to explore. There are questions as to why you alone survived. We will be dispatching a team to your residence once the two of you are out of the city to help in our investigation.”

“Again, that’s just protocol.” Leon tries to reassure, but your mind is whirling. “No-one is accusing you of anything, Dove.”

“I… I’ve worked here for years, I passed all the clearance checks. I wouldn’t, I didn’t…”

“As Agent Kennedy said, it’s just protocol. If you have nothing to hide, there is nothing to fear.” Hunnigan resumes tapping away at the keyboard as she talks, pausing as the computer emits a ping. “SUV’s ready. I suggest you two go.”

Leon gets to his feet, once more offering his hand to help you to yours. He smiles, sympathetically, as he takes in your appearance – your face has lost what little colour it had.

“Time to go, Dove. It’ll be all right.”

You want to say no, you feel like you need to stay to plead your innocence, but you catch sight of the gun holstered by his side and the flame of defiance is extinguished. You take his hand, allowing him to pull you to your feet. He places his hand on the small of your back to guide you back through the door and you can’t work out if it should feel like comfort or a threat.

--

You felt numb as Leon had escorted you to a large SUV with blacked out windows in an empty carpark. He’d opened the door for you, helped you climb in before hesitating.

“Need a hand with your seatbelt?”

You stare at him for a moment too long.

“Because of your arm, I mean.”

“Oh. Please.”

He leans over you, grabbing the seatbelt and clicking it into place.

“Right. Comfy?”

“Yeah.” You swallow. “Thanks.”

He nods, closes the door behind him – softly, you note, rather than a slam and it’s then you realise that you also can’t see out the windows. He hops up into the front, buckles his own seatbelt and starts the engine, swinging the SUV out of the parking space with ease. You can’t really see anything from where you’re sat, bar the back of his head and it must be deliberate.

“Hopefully it’s not too long of a drive.” He comments. “Had one that was a twelve hours’ away once and we are not allowed to stop for bathroom breaks.”

“Are you allowed to tell me how far away it is when you know?”

“Don’t see why not. Hunnigan will ping it through once we’re clear enough.”

It’s hard to tell how much time has passed when, eventually, the promised ping echoes around the car. You can hear him tap his fingers against something and he hums to himself.

“We’re in luck – about two hours away, Dove. Want some music on? Don’t have any CDs but got the radio.”

Maybe the music will help drown out how loud your heart is thudding in your ears. ”Yeah, sure.”

He fiddles with the dial – sound crackling around the car before it settles on some acoustic tune you don’t recognize. Must be some easy listening station.

“You can nap, if you like.”

“Maybe.” Though you’re not sure how you’ll ever sleep again after today.

The rest of the drive passes in silence, apart from the sound of the radio. You close your eyes a few times, leaning your head back against the seat but the creature seems burned into your retinas, haunting your vision.

“This is us.” Leon breaks the silence as you feel the car turn and he reduces the speed. He switches off the car and unclicks his seatbelt, turning back to face you. “Wait there just a moment, okay?”

“Yeah.”

 He smiles, opens his door and hops out, again closing the door softly behind him. What must be a few minutes later, your door opens and he once again offers his hand.

“Ready?

You unclip your seatbelt with your good hand before accepting his outstretched one, helping you step down from the SUV. You’re in a garage now of some sort – spacious enough to fit the car and what looks to be a chest freezer, washer and tumble dryer - the whole room illuminated by an orange bulb.

“So, we said safe house – seems more like a safe bungalow to me. I’ll give you the tour.” He gestures forward towards an open door and you walk forward, once again his hand falling to the small of your back. It leads through to a modest sized kitchen – usual white appliances and opens out into a living room with two couches, a coffee table and an entertainment unit with a television. There are two more doors along the wall, but what really strikes you is how small the windows all are, covered in thick panes of glass.

Bulletproof, you wonder.

“Bathroom’s this one,” he opens the door in demonstration, revealing a typical bathroom, before moving along. “And the bedroom.” It has a double bed, white linen sheets, a wardrobe and dresser. “Your bedroom,” he corrects. “I’ll be on the couch.”

“Oh. Is that comfortable?”

He smiles at your concern. “I’m pretty good at sleeping anywhere, but it looks comfortable enough. Speaking of, it’s pretty late so I think we should call it a night.” He ducks into the bathroom, pulling out a washbag from under the sink and empties the contents on the counter. “Standard toiletries kit to start us off. I’m gonna start bringing in the supplies. Sound good?”

You nod and he heads back towards the garage. You kick off your shoes before you step into the bathroom and close the door, twisting the lock closed. You use the facilities with some difficulty, your first visit since being an arm down, though thankful to be in a dress so as not to battle with trousers. After what some might call a best attempt of washing your hand, you pick up the toothbrush and immediately put it back down in annoyance as you realise you’ll need to deal with the toothpaste first. Thankful for the flip cap, the tube slips from your grip as you squeeze, shooting across the counter and knocking a glass off the counter, sending it smashing to the floor.

“Fu-” The word doesn’t even make it out of your mouth when the door is broken open, slammed against the wall and Leon is stood there, gun raised as you scream.

He scans the room with his eyes, concedes it’s clear and lowers his gun. “What happened? You okay?”

“I… I d-dropped the t-t-toothpaste and smashed the g-glass and…” Your breath catches in your throat again, tears burning in your eyes.

“Hey,” he holsters the gun on his thigh. “Hey, it’s okay, you’re okay. Sorry for scaring you. I thought there was a window in here.”

He looks down at the broken glass that’s exploded over the floor and your sock-clad feet. “Sit down, all right? I’ll clear this up.”

“No, I s-should-”

“I can do it. Just sit, please. I’ll go grab a dustpan – they have one. Not my first safe house.” He soothes, heading off into the kitchen cupboards in search of it.

You sit down on the closed toilet seat lid and wonder bitterly if he’s at more safe houses than his own home. You take the moment to try and settle your breathing, your heart still pounding.

Leon appears at the door once more, grinning as he holds the dustpan and brush aloft in triumph. “Found it.” He crouches down, beginning to sweep up the glass. You watch in silence as he tackles the floor methodically, making sure to brush along each square of bathroom tile until he seems satisfied with his work.

“There. All done.” He places it to the side and grabs the troublesome toothpaste tube, before standing up to his full height. “So, this was the culprit, huh?”

You nod. “I don’t know what happened - the only difference was the toothbrush being on the counter, so I should be able to do it, just-”

He picks up the toothbrush and squeezes a blob of toothpaste on it. “On the house.” Leon jokes, offering it back to you. You stand up and accept it, hesitantly.

“I kinda feel pathetic.” You admit.

“Dove…” You’re getting a little used to the name now. It sounds nice off his tongue – soft and sweet. “You’ve had a shitty day, give yourself a break.”

“No, I mean, it just feels like you’re my servant or something – sweeping up, squeezing out my toothpaste...”

“To protect and serve’s the motto.” He smiles at your confused look. “I was a cop before I was an agent.”

“And this is the stuff you did as a cop?”

“Yes, alongside the helping old ladies with their groceries, helping ducks cross the street…” He teases, before nodding at the toothbrush in your hand. “I’ll leave you to it.”

After brushing your teeth without further incident and taking a few more moments to compose yourself, you exit the bathroom. Leon’s stood at the kitchen counter, paper bag in hand, looking at pill packets. There’s a couple of duffel bags near the garage door, one unzipped.

“Medical notes say it’s painkiller time, I’m afraid.” He grabs a glass from the cupboard, fills it up with water from the tap and places it down besides two white pills. “They’ve given you some sleeping tablets as well, but that’s up to you.”

“Do they stop you dreaming?”

Leon grimaces at your question. “From personal experience, yeah. No dreams.”

You hold out your hand. “Then I’ll take them.”

He nods, shaking another two pills out of a bottle and into his hand, picking up the other two and drops them in your hand. You open your mouth and throw them in, before accepting the glass of water, swallowing it all down.

“So, er, this is gonna be a little bit awkward, but I don’t know what you prefer to sleep in, obviously, but I’m assuming not that.”

“Oh. Yeah, no.”

“So, I pulled out a couple of things.” He nods towards the bedroom, where you can see some items of clothing laying out on the bed. He’s turned the bedside lamp on, the room softly illuminated in a white glow.

“You really are a safe house pro.”

“Ha, yeah.” He grins, rubbing the back of his head. “I guess my question is, do you need a hand with changing? 100% respectful offer, obviously.”

You nod. “Please.”

“Okay. After you.”

You walk into the bedroom, Leon keeping his distance this time. There’s an oversized t-shirt in the pile, looks like it will reach your knees. You pick it up with your good hand, clutching it close to your chest and turn to face him.

“Can you help with the sling?”

“Yep.” He nods – professional, unstrapping it with ease and removing it gently. “Afraid medic says you need to sleep with the sling for a week.”

“Mm.” You nod, hanging your arm down loose before turning around. “I guess if you could unzip and I’ll…”

“Got it.” He tugs down the zipper of your dress slowly – if it was some other encounter you’d say he was being a tease. He stops as he reaches the small of your back, just above your underwear. “What can I do now?”

Your breath hitches in your throat, but there’s no getting around it now. “Any good at undoing a bra? Professionally.”

“Professionally, yep.” You feel gentle fingers deftly unclasp it with ease.

“I think I’ve got it from now until the sling needs back on, so-”

“Say no more. Just call when you’re ready.”

The door closes behind you and you exhale, trying to compose yourself. It’s more months since a man had helped you out of a dress and this, after everything today and the situation you’re in, unsure if he sees you as victim or villain, shouldn’t be making you feel flustered.

Gingerly, you slip one arm out of the dress, followed by the other, wincing as you do so and allowing it to pool down at your feet. Next comes your bra, and then you gently pull the t-shirt over your head, again flinching as your shoulder smarts.

Decent, or decent enough, you call out. “Leon? I’m ready.”

“Coming in.” He announces, pausing a moment before opening the door and immediately moves to pick up the sling from where he placed it on the bed. “I’ll be as gentle as I can.”

With practiced hands, he positions your arm into the sling, adjusting it carefully and fastening it in place once more. “There. Feel okay?”

“Yeah.” You look him in the eyes then – beautiful, blue eyes, before fighting back a yawn. “Thank you.”

“You’re welcome.” He smiles. “That will be the sleeping pills kicking in. I forgot to mention they’re real heavy duty.”

“Mm.” You sit down on the bed then, a little too heavily, before picking up your discarded dress on the floor. “Could you bin this?”

“Of course.” He takes it from you, no question. “Anything else I can do?”

“No. Thank you.”

“You don’t need to keep thanking me, Dove. It’s all right – I told you, part of the job.”

“Still, thank you.” You mumble, head feeling heavy.

“Here,” he pulls back the covers as you scooch yourself back and lean your head back on the pillow, tucking the duvet in over you. “Arm still okay?”

You nod, looking up at him with bleary eyes.

“I swear what happened wasn’t anything to do with me. I swear.”

“Shh,” Leon hushes. “I know.” He feels it in his gut, felt it since the moment he lay eyes on you in Hunnigan’s office. “Maybe tomorrow we’ll hear some updates. But, for now, just sleep. Okay, Dove?”

“Sleep, okay…” You mumble, closing your eyes.

Leon hovers a moment, noting the change in your breathing as the sleeping pills pull you under. He turns off the bedside lamp and leaves the bedroom, quietly, your dress clutched in his hand. He places it in the kitchen bin – there’s an incinerator round the back to erase all trace of their visit, but he’ll do that in the morning.

He makes his way over to the sofa and lies down, not even bothering to remove his boots.

He won’t be sleeping tonight.

-- Do let me know if you'd be interested in a part two! x EDIT: Part two!

Masterlist . Requests welcome . Commissions/Ko-Fi

[carry-on-my-wayward-butt]

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[canmom]

Hypothetical Decentralised Social Media Protocol Stack

if we were to dream up the Next Social Media from first principles we face three problems. one is scaling hosting, the second is discovery/aggregation, the third is moderation.

hosting

hosting for millions of users is very very expensive. you have to have a network of datacentres around the world and mechanisms to sync the data between them. you probably use something like AWS, and they will charge you an eye-watering amount of money for it. since it's so expensive, there's no way to break even except by either charging users to access your service (which people generally hate to do) or selling ads, the ability to intrude on their attention to the highest bidder (which people also hate, and go out of their way to filter out). unless you have a lot of money to burn, this is a major barrier.

the traditional internet hosts everything on different servers, and you use addresses that point you to that server. the problem with this is that it responds poorly to sudden spikes in attention. if you self-host your blog, you can get DDOSed entirely by accident. you can use a service like cloudflare to protect you but that's $$$. you can host a blog on a service like wordpress, or a static site on a service like Github Pages or Neocities, often for free, but that broadly limits interaction to people leaving comments on your blog and doesn't have the off-the-cuff passing-thought sort of interaction that social media does.

the middle ground is forums, which used to be the primary form of social interaction before social media eclipsed them, typically running on one or a few servers with a database + frontend. these are viable enough, often they can be run with fairly minimal ads or by user subscriptions (the SomethingAwful model), but they can't scale indefinitely, and each one is a separate bubble. mastodon is a semi-return to this model, with the addition of a means to use your account on one bubble to interact with another ('federation').

the issue with everything so far is that it's an all-eggs-in-one-basket approach. you depend on the forum, instance, or service paying its bills to stay up. if it goes down, it's just gone. and database-backend models often interact poorly with the internet archive's scraping, so huge chunks won't be preserved.

scaling hosting could theoretically be solved by a model like torrents or IPFS, in which every user becomes a 'server' for all the posts they download, and you look up files using hashes of the content. if a post gets popular, it also gets better seeded! an issue with that design is archival: there is no guarantee that stuff will stay on the network, so if nobody is downloading a post, it is likely to get flushed out by newer stuff. it's like link rot, but it happens automatically.

IPFS solves this by 'pinning': you order an IPFS node (e.g. your server) not to flush a certain file so it will always be available from at least one source. they've sadly mixed this up in cryptocurrency, with 'pinning services' which will take payment in crypto to pin your data. my distaste for a technology designed around red queen races aside, I don't know how pinning costs compare to regular hosting costs.

theoretically you could build a social network on a backbone of content-based addressing. it would come with some drawbacks (posts would be immutable, unless you use some indirection to a traditional address-based hosting) but i think you could make it work (a mix of location-based addressing for low-bandwidth stuff like text, and content-based addressing for inline media). in fact, IPFS has the ability to mix in a bit of address-based lookup into its content-based approach, used for hosting blogs and the like.

as for videos - well, BitTorrent is great for distributing video files. though I don't know how well that scales to something like Youtube. you'd need a lot of hard drive space to handle the amount of Youtube that people typically watch and continue seeding it.

aggregation/discovery

the next problem is aggregation/discovery. social media sites approach this problem in various ways. early social media sites like LiveJournal had a somewhat newsgroup-like approach, you'd join a 'community' and people would post stuff to that community. this got replaced by the subscription model of sites like Twitter and Tumblr, where every user is simultaneously an author and a curator, and you subscribe to someone to see what posts they want to share.

this in turn got replaced by neural network-driven algorithms which attempt to guess what you'll want to see and show you stuff that's popular with whatever it thinks your demographic is. that's gotta go, or at least not be an intrinsic part of the social network anymore.

it would be easy enough to replicate the 'subscribe to see someone's recommended stuff' model, you just need a protocol for pointing people at stuff. (getting analytics such as like/reblog counts would be more difficult!) it would probably look similar to RSS feeds: you upload a list of suitably formatted data, and programs which speak that protocol can download it.

the problem of discovery - ways to find strangers who are interested in the same stuff you are - is more tricky. if we're trying to design this as a fully decentralised, censorship-resistant network, we face the spam problem. any means you use to broadcast 'hi, i exist and i like to talk about this thing, come interact with me' can be subverted by spammers. either you restrict yourself entirely to spreading across a network of curated recommendations, or you have to have moderation.

moderation

moderation is one of the hardest problems of social networks as they currently exist. it's both a problem of spam (the posts that users want to see getting swamped by porn bots or whatever) and legality (they're obliged to remove child porn, beheading videos and the like). the usual solution is a combination of AI shit - does the robot think this looks like a naked person - and outsourcing it to poorly paid workers in (typically) African countries, whose job is to look at reports of the most traumatic shit humans can come up with all day and confirm whether it's bad or not.

for our purposes, the hypothetical decentralised network is a protocol to help computers find stuff, not a platform. we can't control how people use it, and if we're not hosting any of the bad shit, it's not on us. but spam moderation is a problem any time that people can insert content you did not request into your feed.

possibly this is where you could have something like Mastodon instances, with their own moderation rules, but crucially, which don't host the content they aggregate. so instead of having 'an account on an instance', you have a stable address on the network, and you submit it to various directories so people can find you. by keeping each one limited in scale, it makes moderation more feasible. this is basically Reddit's model: you have topic-based hubs which people can subscribe to, and submit stuff to.

the other moderation issue is that there is no mechanism in this design to protect from mass harassment. if someone put you on the K*w*f*rms List of Degenerate Trannies To Suicidebait, there'd be fuck all you can do except refuse to receive contact from strangers. though... that's kind of already true of the internet as it stands. nobody has solved this problem.

to sum up

primarily static sites 'hosted' partly or fully on IPFS and BitTorrent

a protocol for sharing content you want to promote, similar to RSS, that you can aggregate into a 'feed'

directories you can submit posts to which handle their own moderation

no ads, nobody makes money off this

honestly, the biggest problem with all this is mostly just... getting it going in the first place. because let's be real, who but tech nerds is going to use a system that requires you to understand fuckin IPFS? until it's already up and running, this idea's got about as much hope as getting people to sign each others' GPG keys. it would have to have the sharp edges sanded down, so it's as easy to get on the Hypothetical Decentralised Social Network Protocol Stack as it is to register an account on tumblr.

but running over it like this... I don't think it's actually impossible in principle. a lot of the technical hurdles have already been solved. and that's what I want the Next Place to look like.

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