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Customer Communication in Twitter<br />A casestudyof Toyota in a crisis<br />Stefan Stieglitz<br />Nina Krüger<br />Linh D...
Agenda<br />Motivation and Background<br />Related Work<br />Research Design<br />Summary<br />Research Approach for the f...
Change in Public Communication<br />2<br />Customer Communication in Twitter<br />Media (journalists as gatekeepers)<br />...
Trends etc.</li></ul>Viral marketing, undercover actions<br />Social Media<br />
What’s the difference?<br />Opinion leaders are hard to identify<br />Much more data and richer information<br />Possibili...
Research Questions<br />Goals: getting a deeper understanding about the dynamics of the structures of communication, the p...
Agenda<br />Motivation and Background<br />Related Work<br />Research Design<br />Summary<br />Research Approach for the f...
Sentiment in Twitter Messages<br />Sentiment Analysis<br />Sentiment analysis of Tweets: Events in the social, political, ...
Agenda<br />Motivation and Background<br />Related Work<br />Research Design<br />Summary<br />Research Approach for the f...
Proceeding<br />Objects ofstudy: the Top10 players in theautomotiveindustry<br />Identification of appropriate keywords us...
Case selection<br />Identificationof an issue<br />The large-scalecarrecall due to a technical fault in the gas pedals and...
Outline data<br /> 5.870 Tweets withHashtags (1.896 #toyota, 851 #recall) (16%)<br /><ul><li>3.190 Tweets withlinked URLs ...
Findings 				<br />11<br />Customer Communication in Twitter<br />
Sentiment Analysis<br />Classifyingthepolarityof a giventextatthedocument, sentence, orfeature/aspectlevel<br />Linguistic...
Findings <br />13<br />Customer Communication in Twitter<br />Uniform percentage of sentiment words in the discussion<br /...
14<br />Customer Communication in Twitter<br />Findings<br />Isthere a differencebetweensentiments in Tweets createdby pow...
Agenda<br />Motivation and Background<br />Research Approaches<br />Related Work<br />Summary<br />Research Approach for t...
Summary<br />Organization-relatedissuesarediscussed in Twitter<br />Usingtheissuescanningkeywordscanidentifytopicsfortrack...
Agenda<br />Motivation and Background<br />Related Work<br />Research Design<br />Summary<br />Research Approaches for the...
Further Research<br />18<br /><ul><li>Research on dynamics of specific topics in social networks
Comparative studies of different cases
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Customer communication in twitter

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Transcript of "Customer communication in twitter"

  1. 1. Customer Communication in Twitter<br />A casestudyof Toyota in a crisis<br />Stefan Stieglitz<br />Nina Krüger<br />Linh Dang-Xuan<br />DIATA ´11<br />
  2. 2. Agenda<br />Motivation and Background<br />Related Work<br />Research Design<br />Summary<br />Research Approach for the further study<br />1<br />Customer Communication in Twitter<br />
  3. 3. Change in Public Communication<br />2<br />Customer Communication in Twitter<br />Media (journalists as gatekeepers)<br />Agenda Setting<br />Organization <br />(enterprise, political player, …) <br />Public one-way communication<br />Media Monitoring<br />Customers, Citizens<br />Public Relations<br />Public Feedback, Questions, opinions, customer innovation<br />Social Mediamarketing<br />Ideas, Innovations, opinions, Trends, complains, recommendations, etc…<br />Communication between social media users<br /><ul><li>Complains
  4. 4. Trends etc.</li></ul>Viral marketing, undercover actions<br />Social Media<br />
  5. 5. What’s the difference?<br />Opinion leaders are hard to identify<br />Much more data and richer information<br />Possibility to track data automatically and analyze them (digital information, ...) very fast (e.g. direct feedback on campaigns)<br />Everybody has a voice – risks and chances for companies<br />Long tail – opinion gathering<br />Choice of words – no strict rules like in press releases, different styles because of different platforms<br />3<br />Customer Communication in Twitter<br />
  6. 6. Research Questions<br />Goals: getting a deeper understanding about the dynamics of the structures of communication, the participation of the stakeholder and their sentiments in the communication.<br />Are crisis-related issues in twitter discussed (like in the classic media) and are these discussions characterized by peaks and buzzing-stages? Do involved user post higher frequented in peaks than in buzzing stages?<br />Are the postings in the peaks filled with more sentiment-words than in the buzzing stages? <br />Is there a difference between sentiments in Tweets created by power-tweeters (PT) only and the sentiments in Tweets created by all participants of the sample?<br />4<br />Customer Communication in Twitter<br />
  7. 7. Agenda<br />Motivation and Background<br />Related Work<br />Research Design<br />Summary<br />Research Approach for the further study<br />5<br />Customer Communication in Twitter<br />
  8. 8. Sentiment in Twitter Messages<br />Sentiment Analysis<br />Sentiment analysis of Tweets: Events in the social, political, cultural and economic sphere do have a significant, immediate and highly specific effect on the various dimensions of public mood (Bollen et al., 2009). <br />Link measures of public opinion derived from polls to sentiment measured from Twitter messages: Sentiment word frequencies in contemporaneous Twitter messages do correlate with several public opinion time series such as surveys on consumer confidence and political opinion over the 2008 to 2009 period (O’Connor et al., 2010).<br />Study of political tweets around the 2009 German federal election: Tweet sentiment (e.g., positive and negative emotions associated with a politician) corresponds closely to voters’ political preferences(Tumasjanet al., 2010).<br />6<br />Customer Communication in Twitter<br />
  9. 9. Agenda<br />Motivation and Background<br />Related Work<br />Research Design<br />Summary<br />Research Approach for the further study<br />7<br />Customer Communication in Twitter<br />
  10. 10. Proceeding<br />Objects ofstudy: the Top10 players in theautomotiveindustry<br />Identification of appropriate keywords using classic print media:<br />Identification of keywords by scanning the New York Times over a periode of two weeks, analyzing these articles which are related to one of the carmakers.<br />Structural analysis of the course topics: <br />Observation, analysis and documentation of public communication inTwitter using the keywords found with the help of a software prototype<br />Cleaningupthedata<br />8<br />Customer Communication in Twitter<br />
  11. 11. Case selection<br />Identificationof an issue<br />The large-scalecarrecall due to a technical fault in the gas pedals and thebreaks<br />Usingthekeyword-combination „recall/-s“, „Toyota“ <br />Implementation ofthe Issue Scanning fortheperiode 13-31 calendarweek:<br />732.003 Tweets: „Toyota“<br />37.232 Tweets: „recall“ und „Toyota“ <br />9<br />Customer Communication in Twitter<br />
  12. 12. Outline data<br /> 5.870 Tweets withHashtags (1.896 #toyota, 851 #recall) (16%)<br /><ul><li>3.190 Tweets withlinked URLs (8,6%)</li></ul>Relatively uniform distribution of users involved in the communication<br />The 10 mostactive Twitter accountsdid 6.237 postings all in all (17,5 % of all Tweets)<br />Most activeaccount: 1.237 Tweets (Toyota_recall)<br />The twoidentifiedofficial Toyota-accountpublishedonly 237 and 164 Tweets<br />10<br />Customer Communication in Twitter<br />
  13. 13. Findings <br />11<br />Customer Communication in Twitter<br />
  14. 14. Sentiment Analysis<br />Classifyingthepolarityof a giventextatthedocument, sentence, orfeature/aspectlevel<br />Linguisticdimensions<br />Positive emotions (positive feelings, optimism)<br />Negative emotions (anger, anxiety, sadness)<br />Example: Creatingsentimentprofileforcompanies, partiesoraffiliatedindividuals (e.g., in the form of positive/negative-emotion scales)<br />12<br />Customer Communication in Twitter<br />
  15. 15. Findings <br />13<br />Customer Communication in Twitter<br />Uniform percentage of sentiment words in the discussion<br />A clear tendency of a stronger polarization in peaks<br />
  16. 16. 14<br />Customer Communication in Twitter<br />Findings<br />Isthere a differencebetweensentiments in Tweets createdby power-tweeters (PT) only and thesentiments in Tweets createdby all participantsofthesample?<br />
  17. 17. Agenda<br />Motivation and Background<br />Research Approaches<br />Related Work<br />Summary<br />Research Approach for the further study<br />15<br />Customer Communication in Twitter<br />
  18. 18. Summary<br />Organization-relatedissuesarediscussed in Twitter<br />Usingtheissuescanningkeywordscanidentifytopicsfortrackingdynamics<br />In crisis situations, more individuals participate in the discussion (the contribution per user does not rise)<br />In peak periods, there are clear trends in the discussion to positive or negative sentiments<br />Measures may differ in different types of discussion<br />16<br />Customer Communication in Twitter<br />
  19. 19. Agenda<br />Motivation and Background<br />Related Work<br />Research Design<br />Summary<br />Research Approaches for the further study<br />17<br />Customer Communication in Twitter<br />
  20. 20. Further Research<br />18<br /><ul><li>Research on dynamics of specific topics in social networks
  21. 21. Comparative studies of different cases
  22. 22. Content analysis of the Tweets
  23. 23. Social Network analysis </li></ul>Customer Communication in Twitter<br />
  24. 24. Many thanks for your attention!<br />19<br />Customer Communication in Twitter<br />
  25. 25. 28.09.2011<br />20<br />Nina Krüger M.A.<br />nina.krueger@wi.uni-muenster.de<br />0251- 83 38 014<br />
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