Searching Twitter: Separating the Tweet from the Chaff

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This presentation was given at ICWSM 2011. In this presentation, we report on a qualitative investigation into the different factors that make tweets ‘useful’ and ‘not useful’ for a set of common search tasks. The investigation found 16 features that help make a tweet useful, noting that useful tweets often showed 2 or 3 of these features. ‘Not useful’ tweets, however, typically had only one of 17 clear and striking features.Our results contribute a novel framework for extracting useful information from real-time streams of social-media content

Transcript of Searching Twitter: Separating the Tweet from the Chaff

  • 1.Searching Twitter:Separating the Tweetfrom the ChaffJonathan Hurlock & Max L. Wilson

2. You sure can! 3. llowI fo ? do ests H ow ter y In mhttp://www.ickr.com/photos/stevegareld/5397972626/ 4. http://www.ickr.com/photos/apelad/3684843147/ 5. Yet more DataMeta Data, Prole Data, Linked Data 6. Any of it Useful?Who cares how much data there is!I think the challenge not only for twitter, but forthe technology industry at large. Is buildingmore relevant lters, in real time. Like beingable to surface valuable informationimmediately. No matter who it is, whoslistening or whos broadcasting, is a reallyreally hard problem, and it makes twitter alotmore meaningful[... ]Weve gotten really reallygood at being able to put content in, into media[...] getting it out in a relevant, valueable way,in real time is still very difcult.- Jack Dorsey (Creator of Twitter) 7. Why Twitter?Where is the value? $ ! ! ! ! ! $ 8. Lets go back...http://www.ickr.com/photos/milesdeelite/5309712846/ 9. Lets go back...Great Scott!http://www.ickr.com/photos/milesdeelite/5309712846/ 10. Asking FriendsHey, what are you doing? you & me 11. Social SearchWhat is everyone else doing? you & me 12. friendfriend friendSocial SearchWhat is everyone else doing? friendyou&me 13. bob &lisaExisting KnowledgeNo need to reinvent the wheelyou& meMeredith Ringel Morris, Jaime Teevan, and Katrina Panovich. 2010. What do people ask their social networks, and why?: asurvey study of status message & behavior. In Proceedings of the 28th international conference on Human factors incomputing systems (CHI 10). ACM, New York, NY, USA, 1739-1748. 14. lisaExisting Knowledge bob &meNo need to reinvent the wheelyouMeredith Ringel Morris, Jaime Teevan, and Katrina Panovich. 2010. What do people ask their social networks, and why?: asurvey study of status message & behavior. In Proceedings of the 28th international conference on Human factors incomputing systems (CHI 10). ACM, New York, NY, USA, 1739-1748. 15. Lets go back to the networkRemember... you & me 16. friend friendfriendand if we take a step back...Please mind the gap friendyou me 17. We start to see interesting things... 18. Which have value! 19. Location, experiences, temporal data Yardi, Sarita and Boyd, Danah. ICWSM 2010.http://www.ickr.com/photos/24423474@N08/4999891492/http://www.ickr.com/photos/mdid/4560003881/ Tweeting from the Town Square: Measuring Geographichttp://www.ickr.com/photos/seanhobson/3256437306/ Local Networkshttp://www.ickr.com/photos/gcaw/5445225362/http://en.wikipedia.org/wiki/File:Plane_crash_into_Hudson_River_(crop).jpg 20. Location, experiences, temporal data Political upheaval, emergency events .. so what are you tweeting now? Yardi, Sarita and Boyd, Danah. ICWSM 2010.http://www.ickr.com/photos/24423474@N08/4999891492/http://www.ickr.com/photos/mdid/4560003881/ Tweeting from the Town Square: Measuring Geographichttp://www.ickr.com/photos/seanhobson/3256437306/ Local Networkshttp://www.ickr.com/photos/gcaw/5445225362/http://en.wikipedia.org/wiki/File:Plane_crash_into_Hudson_River_(crop).jpg 21. Twitter SearchHow do you nd useful information? 22. Displaying ResultsRealtime 23. Displaying ResultsRTTime, ReTweets, Location, Popularity?http://www.ickr.com/photos/publicenergy/394124407/ 24. Displaying ResultsRTTime, ReTweets, Location, Popularity?http://www.ickr.com/photos/publicenergy/394124407/ 25. Displaying ResultsMaking sense of the data. 26. Displaying Results Making sense of the data.Michael S. Bernstein, Bongwon Suh, Lichan Hong, Jilin Chen, Sanjay Kairam, Ed H. Chi. Eddi: Interactive Topic-based Browsing of Social Status Streams.In Proc. of ACM User Interface Software and Technology (UIST) conference, Oct. 2010. New York, NY. 27. Displaying Results Making sense of the data.Diakopoulos, N.; Naaman, M.; Kivran-Swaine, F.; , "Diamonds in the rough: Social media visual analytics for journalistic inquiry,"Visual Analytics Science and Technology (VAST), 2010 IEEE Symposium on , vol., no., pp.115-122, 25-26 Oct. 2010 28. Interestingness Not necessarily useful!Naveed, Nasir and Gottron, Thomas and Kunegis, Jrme and Alhadi, Arifah Che (2011) Bad News Travel Fast: A Content-based Analysis of Interestingness on Twitter. pp. 1-7. In: Proceedings of the ACM WebSci11, June 14-17 2011, Koblenz, Germany.http://www.ickr.com/photos/wwarby/2460655511/ 29. How we are different?What makes us unique? 30. Finding Usefulness! What constitutes a useful Tweet?fuln essusehttp://www.ickr.com/photos/edduddiee/4346349664/ 31. The MethodHow did we go about this? 32. Teevan, J., Ramage, D., & Morris, M. R. (2011). #TwitterSearch: a comparison of microblog search and web search. WSDM11: Proceedings of the fourth ACM international conference on Web search and data mining (pp. 35-44). New York, NY, USA:ACM.Information Seeking3 Information Seeking Taskshttp://www.bbc.co.uk/proms/2010/share/badgewidget.shtmlhttp://www.ickr.com/photos/ivyeld/4731067396/http://www.ickr.com/photos/anniemole/241655156/ 33. 20 ParticipantsThey were really nice people! 34. Search InterfaceA simple, easy to understand interface 35. Its useful because...Think aloud + InterviewsTo help us provide more insightI didnt because... 36. AnalysisLots and lots of it! K 37. Grounded Theory Inductive Coding = Lots of Post-its!Glaser, B. G., & Strauss, A. L. (2009).The Discovery of Grounded Theory: strategies for qualitative research.Piscataway, New Jersey, USA: Transaction Publishers. 38. Kappa Analysis Cohen... Fleiss....Landis, R. J., & Koch, G. G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics , 33 (1),159-174. 39. Extended Kappa Analysis Multi Coded Kappa0.73 (Substantial Agreement) Between Evaluators &0.62 (Substantial Agreement) with Independent Untrained CoderHarris, J. K., & Burke, R. C. (2005). Do you see what I see? An application of inter-coder reliability in qualitative analysis.American Public Health Association 133rd Annual Meeting & Exposition. Washington, DC, USA: American Public HealthAssociation. 40. What did we nd?Useful & Not-Useful 41. UsefulIn Tweet Content ExperienceSomeone reporting a personal experience, but not necessarily suggestion / direction.Direct Someone making a direct recommendation, but not necessarily relaying a personal experience.RecommendationSocial Knowledge Containing information that is spreading socially, or becoming general knowledge. Specic InformationWhere facts are listed directly in tweets e.g. prices, times etc.Reection on Tweet EntertainingThe reader nds them amusing.Shared Sentiment The reader agrees with the author of the tweet.RelevantTimeThe time is current LocationThe location is relevant to the query. 42. Useful (cont.)TrustTrusted Author The twitter account has a reputation / followingTrusted AvatarThe visual appearance cultivates trust.Trusted Link A link to a trustworthy recognisable domain.LinksActionable Link The user can perform a transaction by using the link (heavily dependent on trust)Media LinkThe link is to rich multimedia content.Useful Link The link provides valuable information content, e.g. authoritative information, educated reviewsMeta Tweet ReTweeted Lots Its information that others have passed on lots Conversation Its part of a series of tweets, and they all need to be useful 43. Not UsefulTweet ContentNo InformationAbsence of anything, event, factual points IntrospectivePersonal content and personal thoughts for no social benet Off Topic Result not related to the query give / TF-IDF irrelevant Too Technical The content requires specic domain knowledge the resader doesnt possessPoorly Constructed Tweets that may have grammatical / spelling errors, or malformed URLs.Bad Tweets SPAM Irrelevant or inappropriate messages Wrong Language Messages sent in a foreign language of that to the reader Dead Link A URL which does not work i.e. a 404Not Relevant Time Out of date content LocationWrong geographic location 44. Not Useful (cont.)TrustUn-truested AuthorAn author the reader feels at un-eased by or suspicious of.Un-trusted Link A link the reader feels is suspiciousSubjectiveA tweet that is perspective centric, meaning the author is providing their view or projecting an Perspective Orientedattitude on a subject matter or to a subject / reader. Disagree with Tweet A conict of aggreement between the reader and the authorNot FunnyA tweet that is aimed to be humorous, which the reader does not feel is humorous.Meta TweetQnAPart of a conversation, reader desires the whole convo. not just the question or the answer.Repeated Content the reader has seen before. 45. Insights Interesting ndshttp://www.ickr.com/photos/foxmulderven/3063598624 46. The Possible ImpactWhere could we see the impact of this work? 47. Search SystemA work in progress 48. ConclusionsSo just remember. 49. Thank you for ListeningJonathan Hurlock @jonhurlockMax L. Wilson @gingdottwitLike the talk? Then please tweet it, by quickly visiting:http://moourl.com/LikedTheTalk