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Reviewing initial proposal & reflection

Background research / Previous Project Brief

In the previous assignment, I conducted a study on users who used Twitter to do fan activities. I got into the way Twitter is used for fan activities and its algorithms, so I decided to do further about this topic which is about fangirling and fanboying for the project as well and also decided to produce interesting tools that can support and their fan activities using twitter.

A conceptual outline / creative vision

After weeks of research and observation, I have defined several elements of convenience on Twitter that are used for fan activities.

1. Simple, intuitive search function 2. High readability and transferability due to the 140 words limit 3. Retweets, bookmarks, moments, lists, and favorites functions are effective in fan activities

The initial idea of this project is to make the most of these Twitter functions and visualize these elements to add a sense of aesthetics to the outcome

So a web platform will be produced using html, css, and JavaScript, which will target fans of celebrities who are active in their fan activities with twitter.

+ python, R studio, Microsoft Excel, jQuery

To briefly explain the plan for the screen configuration of the website, there is a search bar at the top of the screen where you can search for the names of your favorite entertainers. Below it,  keyword boxes existing in various sizes and shapes will be placed, and the composition and design of these boxes will be designed with reference to the Dada style. The difference in font size and shape in each keyword will add aesthetic elements to the website. If a user clicks on a keyword, they can go directly to Twitter search results. The font size of the keyword depends on the frequency of the user's search.

Functions / aesthetics

As for the Function - i am going to Randomly select specific singers, make a list, and select approximately 10 keywords for one singer/or group that are most searched by their fans and gather them together. The collected keywords are placed in a box and arranged randomly again. You can click on a keyword to easily navigate to the Twitter search results screen for that keyword.

In terms of Aesthetics - It's meaningful in that it's for fans' convenience but also to give them an unusual experience. The arrangement of keywords is randomly placed with design elements so visitors can have a unique feelings. Popular keywords will be appeared in large fonts, and relatively less popular keywords will be placed in small fonts to enhance intuition and add aesthetic elements. 

- be done with R Studio

Technical and learning requirements

research and learning that should be done in advance - what I need to do before I start developing the web it to choose singers and select the keywords most searched for them by their fans. Also I need to conduct a research on twitter keyword search results.

Production tool - most of the work will be conducted with html5 and css coding with a help of bootstrap and javascript to add design elements. The Web will be optimized for a 15-inch MacBook Pro, but it can respond to other devices as well. 

+ python, R studio, Microsoft Excel, jQuery

reflection EOC ;

During this task, I was able to learn new skills  by trying programs that I had never used before, and i still have far way to go but through many trials and errors i could get confidence.  I will continue to practice even after the end of the semester so that I don't lose what I learned from this course.

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written reflection

The initial purpose of the project development was "information visualization", which is the same as it is now.

Thus, My final project achieves the goal and aim of the initial idea. However,  there was some significant changes of the project during the development process.

what i planned at the very first stage was The web where the tweet appears on the web when you click on each keyword, and i wanted to apply some DADA style to my web, so I did some research on typography as well.

But my idea of the Dada style web, after all, turns into the idea of data crawling, mining and visualization. The process of using the r studio to collect data and visualize the collected data with wordcloud was difficult, but it was worth studying because I could learn a lot through trial and error.

Another important change was the way Twitter timelines and tweets that were supposed to be embedded  into the Web were just linked directly. And the purpose of that was to create a simple and unified web without useless and unifying elements.

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Generated twitterbot - autotweet function

add an additional automatic-retweet function

Python https://www.linkedin.com/learning-login/share?forceAccount=false&redirect=https%3A%2F%2Fwww.linkedin.com%2Flearning%2Fprogramming-foundations-fundamentals-3%3Ftrk%3Dshare_ent_url&account=2104756

https://www.linkedin.com/learning-login/share?forceAccount=false&redirect=https%3A%2F%2Fwww.linkedin.com%2Flearning%2Fpython-essential-training-2%3Ftrk%3Dshare_ent_url&account=2104756

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reflection ;

what are the differences between initial concept & final Project?

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WordCloud Keywords (in the order of "most searched keyword")

twice, iu, bts, blackpink, exo, nct

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#idolsforidols Project Functions

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Project Typefaces (Playful Display, courier, Arial Black)

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R Studio - metadatasearch

install.packages("wordcloud")

library(wordcloud)

install.packages("RColorBrewer")

library(RColorBrewer)

install.packages("wordcloud2)

library(wordcloud2)

install.packages("twitteR")

library(twitteR)

install.packages("ROAuth")

library(ROAuth)

install.packages("stringr")

library(stringr)

consumer_key = 'qRalqyd82SZB9igMzBECb2chN'

consumer_secret = 'WA9uxViYSChDlqYb3x93j6jHcakGpCbdsKDp3QmSwnbu2JTfa9'

access_token = '1316551448222552065-ONmzpeVRlGmkWslKAbEIqGD9pC7VpO'

access_secret = 'ZAjPVroSfgjDt4YflVIAheg4UuQcDgP76CKkaLgaOjcQj'

setup_twitter_oauth(consumer_key, consumer_secret, access_token, access_secret)

exo.tweets <- searchTwitter(‘exo', n=200, lang = 'en’, include_rts = FALSE)

bts.tweets <- searchTwitter(‘bts’, n=200, lang = 'en', include_rts = FALSE)

iu.tweets <- searchTwitter(‘iu’, n=200, lang = 'en', include_rts = FALSE)

twice.tweets <- searchTwitter(‘twice’, n=200, lang = 'en')

nct.tweets <- searchTwitter(‘nct’, n=200, lang = 'en', include_rts = FALSE)

blackpink.tweets <- searchTwitter(‘blackpink’, n=200, lang = 'en', include_rts = FALSE)

matchtweets = searchTwitter("twice", n=200, lang="en”)

matchtweets = searchTwitter("twice", n=200, lang="en”)

twice_en < searchTwitter(keyword_en, n=200, lang=“en”, since="2020-01-01", until="2020-10-12")

searchTwitter("#bts", n=100)

searchTwitter(“bts”, n=1000, lang="en")

myCorpus = Corpus(VectorSource(matchText))

myCorpus

wordcloud(bts

+           , scale=c(5,0.5)     # Set min and max scale

+           , max.words=100      # Set top n words

+           , random.order=FALSE # Words in decreasing freq

+           , rot.per=0.35       # % of vertical words

+           , use.r.layout=FALSE # Use C++ collision detection

+           , colors=brewer.pal(8, "Dark2"))

#Grab tweets - note: reduce to 1000 if it's slow

hmt <- search_tweets(“#bts”, n = 1000, include_rts = FALSE)

searchTwitter(“#bts”, n=100, lang="en")

mach_tweets = searchTwitter(“#exo, n=500, lang="en”, resultType=“recent”)

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Useful Code - Jquary

<script src="css/https;//code.jquery.com/jquery-3.2.1.js"></script>

<a href="https://twitter.com/intent/tweet?button_hashtag=bts&ref_src=twsrc%5Etfw"

class="twitter-hashtag-button" data-show-count="false">

Tweet #bts</a>

<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

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DMS Project (API and Web)

Kr / En method archive

1. 트위터 api, 메타데이터 수집

1단계 - 트위터 개발자 계정 승인 Twitter Developer Account Authorization

2단계 - 파이선과 트위피를 사용하여 간단한 트윗 봇 개발 Use Python and Tweepy to Develop a Simple Tweet Bot

3단계 - r studio를 사용하여 메타 데이터 수집 Use r Studio to collect meta data / I used hashtag to search data, and collected 500 words per selected group - black pink, twice, bts, exo and nct. 

4단계 - 수집된 메타 데이터를 텍스트로 저장 Save collected meta data as text 

5단계 - 워드클라우드 프로그램을 사용하여 “가장 많이 검색된 빈도”순서로 시각화 use a wordcloud program to visualize it in the order of "most searched keyword"

2. 웹사이트 개발 Build a Web

Step 1 - Develop Intro Page

Step 2 - Develop the main page and link it to the intro page.

Step 3 - Link main page to Twitter search results

Step 4 - Tweet bot's timeline was added to the web page in the form of embedded timeline (removed)

1단계 - 인트로 페이지 개발

2단계 - 메인 페이지 개발 후 인트로 페이지와 연동

3단계 - 메인 페이지와 트위터 검색 결과 연동

4단계 - 트윗봇의 타임라인을 웹 페이지에 embedded timeline의 형태로 추가

(4단계 - 추후에 제거)

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Useful Codes -  twitterscraper

from twitterscraper.query import query_user_info

twitter_user = "BTS_twt"

user_info = query_user_info(user= twitter_user)

print("user : "+user_info.user)

print("fullname : "+user_info.full_name)

print("location : "+user_info.location)

print("blog : "+user_info.location)

print("date_joined : "+user_info.date_joined)

print("id : "+user_info.id)

print("num_of_tweets : "+str(user_info.tweets))

print("following : "+str(user_info.following))

print("follower : "+str(user_info.followers))

print("likes : "+str(user_info.likes))

print("lists : "+str(user_info.lists))

from twitterscraper.query import query_tweets_from_user

twitter_user = "BTS_twt"

list_of_tweets = query_tweets_from_user(twitter_user, limit=10)

for tweet in list_of_tweets:

   print("timestamp: "+str(tweet.timestamp))

   print("text: "+tweet.text)

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Useful Codes - Tweepy

import unittest

if __name__ == '__main__':

   testsuite = unittest.TestLoader().discover('.')

   unittest.TextTestRunner(verbosity=1).run(testsuite)

pip install tweepy

import tweepy

def twitting(message):

   apikey = 'qRalqyd82SZB9igMzBECb2chN'

   apisecret = 'WA9uxViYSChDlqYb3x93j6jHcakGpCbdsKDp3QmSwnbu2JTfa9'

   access_token = '1316551448222552065-ONmzpeVRlGmkWslKAbEIqGD9pC7VpO'

   access_token_secret = 'ZAjPVroSfgjDt4YflVIAheg4UuQcDgP76CKkaLgaOjcQj'

   auth = tweepy.OAuthHandler(apikey, apisecret)

   auth.set_access_token(access_token,access_token_secret)

   api = tweepy.API(auth)

   api.update_status(message)