1. Role of Online Social Networks during disasters & political movements Saptarshi Ghosh Department of Computer Science and Technology Bengal Engineering and Science University Shibpur

2. Online Social Networks (OSNs) Among the most popular sites in today’s Web  More than few billion users world-wide  Celebrities, media houses, politicians all using OSNs  Quick ways of disseminating information, real-time news Huge data readily available  Plethora of user-generated content: text, images, videos, …  Automated means of collecting data rather than surveys (on which traditional social media research had to depend)

3. Variety in online social media

4. Multi-disciplinary research on OSNs Tools from a wide variety of disciplines used to study OSNs  Sociology – how human beings behave in society  Computer networks & distributed systems  Network science, complex network theory  Data mining, machine learning, information retrieval, natural language processing, …

5. Mining information on recent events Facebook, Twitter are valuable sources of news on events happening ‘now’ [Yardi, ICWSM 2010]  Natural calamities, e.g., hurricanes, floods, earthquakes [Sakaki, WWW 2010][Qu, CSCW 2011]  Man-made calamities, e.g., bomb blasts, riots  Spread of epidemics, e.g., dengue [Gomide, WebSci 2011]  Elections, political unrests

6. OSNs after calamities

8. No longer only a comic strip, but close to reality Sakaki et. al., “Earthquake shakes Twitter users: real-time event detection by social sensors”, WWW 2010 Activity in Twitter after earthquake

9. Profile of a Twitter user

10. Example tweets

11. Use of OSNs during & after disasters Qu et al. Microblogging after a Major Disaster in China: A Case Study of the 2010 Yushu Earthquake. CSCW 2011  Analyed citizens’ activity in such scenario  How information spreads  How microblogging facilitated disaster response Muralidharan, Rasmussen. Hope for Haiti: An analysis of Facebook and Twitter usage during the earthquake relief efforts. Public Relations Review (Science Direct)  Analyzed tweets posted by media & non-profit organizations

12. Types of posts / tweets Different types of tweets posted during & after disasters  Situational Updates  Opinion and sentimental tweets  Help Tweets  Event Analysis

13. Types of tweets Situational update

14. Types of tweets Help Tweet

15. Types of tweets Opinion and Sentiment

16. Types of tweets Event analysis

17. Utilizing information in OSNs During an important event, posts generated in OSNs at the rate of hundreds to thousands per second Several well-known challenges / research issues  Extracting important information  Summarization of data  Authority / expert identification  Public sentiment / opinion mining  Spam detection  Dealing with misinformation, rumours  … and many others

18. Extracting important information Important information during a calamity  Situational updates (SU)  How to identify SU posts from among all posts? Use of NLP and ML techniques [Vieweg, ICWSM 2011]  NLP to identify objectivity, formal / informal register, personal / impersonal tone of tweets  Trained ML classifier based on these features  85% - 90% accuracy in SU / non-SU classification

19. Summarization of data Tweets posted too fast for human comprehension  Ways to organize data: extract important posts, automatic summarization, … Summarization of sets of tweets on a common topic [Sharifi, HLT-NAACL 2010][Inouye, SocialCom, 2011] Continuous summarization of tweet streams [Shou, SIGIR 2013]

20. Identify influential users / experts Several metrics of influence [Cha, ICWSM 2010]  #followers, PageRank, #times retweeted in Twitter, … Topical experts [Weng, WSDM 2010] [Pal, WSDM 2011] [Ghosh, SIGIR 2012]  Authoritative sources of information on specific topics  How to measure topic-specific expertise of users? Experts during specific events  Community leaders during emergencies [Tyshchuk, ASONAM 2013]  Geographically ‘local’ sources [Yardi, ICWSM 2007]

21. Emotion / opinion mining Identify user’s emotion / opinion from posts in OSN [Bollen, WWW 2010]  Identify opinion on movies / political issues [Fang, WSDM 2012]  Summarization of opinions [Ganesan, WWW 2012]  Twitter used to predict success of movies, election results [Tumasjan, ICWSM 2010]  Extension: estimate sentiment of a country / whole world on issues of national / international importance

22. Spam detection Identify spam / users with malicious intentions  Identify spam in Facebook [Gao, IMC 2010],Twitter [Lee, SIGIR 2010], Youtube [Benevenuto, SIGIR 2009], blogs [Shin, Infocom 2011], …  Identify spam in tweets related to trending topics / events happening now [Benevenuto, CEAS 2010][Martinez-Romo, Expert Systems 2013]  Sybil detection [Yu, SIGCOMM 2006][Viswanath, SIGCOMM 2010]

23. Dealing with rumor / misinformation Rumors frequently posted, often unintentionally Detecting rumors in tweets [Gupta, PSOSM 2012][Castillo, WWW 2011]  Classify credible / non-credible, rank tweets wrt credibility  Features: text-based (swear words, emoticons), user- based (#followers, retweeting behavior), how propagated Spread of rumors in social networks  Why rumors spread quickly [Doerr, ACM Communications, 2012]  How to control rumors [Tripathy, CIKM 2010]

24. Rumors in Twitter after London riots http://www.guardian.co.uk/uk/interactive/2011/dec/07/london-riots-twitter

25. Thank You Contact: sghosh@cs.becs.ac.in