Analysis and Visualization of Real-Time Twitter Data

1 W I S S E N T E C H N I K L E I D E N S C H A F T

www.tugraz.at

Analysis and Visualization of Real-Time Twitter Data

19.11.2015 Sead Harmandic

2 2 Motivation

• Social Network and Networking • Micro-Blogging

• Twitter • Launched in 2006 • Active users per month

• ~ 316 Milions (August) • ~ 320 Milions (current)

• Tweets per day ~ 500 Milions


19.11.2015 Sead HARMANDIC

3 3 Problem and Research Objective

• Problems with Twitter • Event based data • Detail event information • Collection of information

• Research Objective

• What kind or sort of information are we capable of providing during and after Twitter event?



4 4 State of the Art I Analyse Twitter as a form of electronic word-of-mouth in correlation to brands and the influence of the service on various brands. [Jansen et al., 2009] • Brands

• H&M, Honda, Exxon, Dell, Lenovo, Amazon, etc. • Opinion (sentiment)

• None; Wretched ; Bad; So-So; Swell; Great



5 5 State of the Art II Using Twitter and (classified) real-time data in order to notify the public about the eathquake. [Sakaki et al., 2010] • Test region: Japan

• Large ammount of Twitter users • High rate of earthquakes per year

• Twitter user sensor • Tweet sensor information (social sensor)

• Toretter („we have taken it“) since 2010 • Faster then Japan Meteorogical Agency



6 6 Available Tools

• TweetTracker • Pros: Geo. Maps; Translation of Non-English; Keyword

comparison • Cons: Visualizing up to 7500 Tweets

• TweetArchivist • Pros: Top Users; Top Hashtags; Language • Cons: No storage or APIs, Paid service

• twExplorer • Pros: Top Users; Top Hashtags • Cons: No archiv or APIs, Maximum of 500 Tweets



7 7 TwitterSuitcase

• Why Suitcase • Identification • Objective

• TU Graz Twitter Applications

• TweetCollector (raw Twitter data) • TwitterWall (event representation) • TwitterStat (analysis of keyword, hashtag or person) • TweetGraph (scope of tweets) • TwitterSuitcase



8 8 TwitterSuitcase - Concept Analysis and Visualization of Real-Time Twitter Data


9 9 TwitterSuitcase – Overview Analysis and Visualization of Real-Time Twitter Data


10 10 TwitterSuitcase – Categories I Analysis and Visualization of Real-Time Twitter Data


• Top Users

11 11 TwitterSuitcase – Categories II Analysis and Visualization of Real-Time Twitter Data


• Top Links

12 12 TwitterSuitcase – Categories III Analysis and Visualization of Real-Time Twitter Data


• Most Popular Retweets

13 13 TwitterSuitcase – Categories IV Analysis and Visualization of Real-Time Twitter Data


• Timeline

14 14 TwitterSuitcase – Categories V Analysis and Visualization of Real-Time Twitter Data


• Top Words

15 15 TwitterSuitcase – Categories VI Analysis and Visualization of Real-Time Twitter Data


• Top Software

16 16 TwitterSuitcase – Categories VII Analysis and Visualization of Real-Time Twitter Data


• Most Popular Hashtags

17 17 TwitterSuitcase – Categories VIII Analysis and Visualization of Real-Time Twitter Data


• Top Screenshots

18 18 TwitterSuitcase – Categories IX Analysis and Visualization of Real-Time Twitter Data


• Wikipedia Article(s)

19 19 TwitterSuitcase – Use Case I

• European Massive Open Online Courses • #emoocs2014

• Total of 4450 Tweets



20 20 TwitterSuitcase – Use Case II

• Most Popular Hashtags



21 21 TwitterSuitcase – Use Case III

• Top Users • moocf(185), Agora Sup(141), fuscia info(134), pabloachard(124),

mooc24(120), tkoscielniak(103), bobreuter(85), OpenEduEU(84), yveszieba(81), redasadki(81) ~ 25%

• Top Link(s) • http://bit.ly/1la3yJX (32) HTML Page „eLearning Papers Issue 37“

• Top Words • RT(2567), moocs(802), mooc(639), learning(339) and openedueu(319).

The rest of the words belong mostly to prepositions or articles.

• Used Software • Web(1574 or 35.4%), Apple devices(1253 or 28.5%), TweetDeck(564 or

12.7%), Android devices(288 or 6.5%)



http://bit.ly/1la3yJX

22 22 Conclusion

• TwitterSuitcase • Research objective What kind or sort of information are we capable of

providing during and after Twitter event?

• TwitterSuitcase extensions • Visualizing Tweets on Geographical Maps; Region-Tweet-Search • MentionMaps

• ReTweets; HTTP Links; Data sources; etc.



23 23

Thank you for your attention.



24 24 Bibliography

[Java et al., 2007] Java, A., Song, X., Finin, T., and Tseng, B. (2007). Why we twitter: Understanding microblogging usage and communities. Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pages 56–65. [Jansen et al., 2009] Jansen, B. J., Zhang, M., Sobel, K., and Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American society for information science and technology, page 2169–2188. [Sakaki et al., 2010] Sakaki, T., Okazaki, M., and Matsuo, Y. (2010). Earthquake shakes twitter users: real-time event detection by social sensors. Proceedings of the 19th international conference on World wide web, pages 851–860.



Analysis and Visualization of Real-Time Twitter Data

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