'

Analysis and Visualization of Real-Time Twitter Data

Понравилась презентация – покажи это...





Слайд 0

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 1 Analysis and Visualization of Real-Time Twitter Data Sead Harmandic 19.11.2015  www.tugraz.at


Слайд 1

Analysis and Visualization of Real-Time Twitter Data 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 Sead HARMANDIC 19.11.2015


Слайд 2

Analysis and Visualization of Real-Time Twitter Data 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? Sead HARMANDIC 19.11.2015


Слайд 3

Analysis and Visualization of Real-Time Twitter Data 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 Sead HARMANDIC 19.11.2015


Слайд 4

Analysis and Visualization of Real-Time Twitter Data 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 Sead HARMANDIC 19.11.2015


Слайд 5

Analysis and Visualization of Real-Time Twitter Data 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 Sead HARMANDIC 19.11.2015


Слайд 6

Analysis and Visualization of Real-Time Twitter Data 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 Sead HARMANDIC 19.11.2015


Слайд 7

Analysis and Visualization of Real-Time Twitter Data 8 TwitterSuitcase - Concept Sead HARMANDIC 19.11.2015


Слайд 8

Analysis and Visualization of Real-Time Twitter Data 9 TwitterSuitcase – Overview Sead HARMANDIC 19.11.2015


Слайд 9

Analysis and Visualization of Real-Time Twitter Data 10 TwitterSuitcase – Categories I • Top Users Sead HARMANDIC 19.11.2015


Слайд 10

Analysis and Visualization of Real-Time Twitter Data 11 TwitterSuitcase – Categories II • Top Links Sead HARMANDIC 19.11.2015


Слайд 11

Analysis and Visualization of Real-Time Twitter Data 12 TwitterSuitcase – Categories III • Most Popular Retweets Sead HARMANDIC 19.11.2015


Слайд 12

Analysis and Visualization of Real-Time Twitter Data 13 TwitterSuitcase – Categories IV • Timeline Sead HARMANDIC 19.11.2015


Слайд 13

Analysis and Visualization of Real-Time Twitter Data 14 TwitterSuitcase – Categories V • Top Words Sead HARMANDIC 19.11.2015


Слайд 14

Analysis and Visualization of Real-Time Twitter Data 15 TwitterSuitcase – Categories VI • Top Software Sead HARMANDIC 19.11.2015


Слайд 15

Analysis and Visualization of Real-Time Twitter Data 16 TwitterSuitcase – Categories VII • Most Popular Hashtags Sead HARMANDIC 19.11.2015


Слайд 16

Analysis and Visualization of Real-Time Twitter Data 17 TwitterSuitcase – Categories VIII • Top Screenshots Sead HARMANDIC 19.11.2015


Слайд 17

Analysis and Visualization of Real-Time Twitter Data 18 TwitterSuitcase – Categories IX • Wikipedia Article(s) Sead HARMANDIC 19.11.2015


Слайд 18

Analysis and Visualization of Real-Time Twitter Data 19 TwitterSuitcase – Use Case I • European Massive Open Online Courses • • #emoocs2014 Total of 4450 Tweets Sead HARMANDIC 19.11.2015


Слайд 19

Analysis and Visualization of Real-Time Twitter Data 20 TwitterSuitcase – Use Case II • Most Popular Hashtags Sead HARMANDIC 19.11.2015


Слайд 20

Analysis and Visualization of Real-Time Twitter Data 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%) Sead HARMANDIC 19.11.2015


Слайд 21

Analysis and Visualization of Real-Time Twitter Data 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. Sead HARMANDIC 19.11.2015


Слайд 22

Analysis and Visualization of Real-Time Twitter Data 23 Thank you for your attention. Sead HARMANDIC 19.11.2015


Слайд 23

Analysis and Visualization of Real-Time Twitter Data 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. Sead HARMANDIC 19.11.2015


Слайд 24


×

HTML:





Ссылка: