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Detecting Influencers in Multiple Online Genres

Authors:

Sara Rosenthal,

Kathleen MckeownAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 17, Issue 2

Article No.: 12, Pages 1 - 22

https://doi.org/10.1145/3014164

Published: 23 March 2017 Publication History

Abstract

Social media has become very popular and mainstream, leading to an abundance of content. This wealth of content contains many interactions and conversations that can be analyzed for a variety of information. One such type of information is analyzing the roles people take in a conversation. Detecting influencers, one such role, can be useful for political campaigning, successful advertisement strategies, and detecting terrorist leaders. We explore influence in discussion forums, weblogs, and micro-blogs through the development of learned language analysis components to recognize known indicators of influence. Our components are author traits, agreement, claims, argumentation, persuasion, credibility, and certain dialog patterns. Each of these components is motivated by social science through Robert Cialdini’s “Weapons of Influence” [Cialdini 2007]. We classify influencers across five online genres and analyze which features are most indicative of influencers in each genre. First, we describe a rich suite of features that were generated using each of the system components. Then, we describe our experiments and results, including using domain adaptation to exploit the data from multiple online genres.

References

[1]

Rob Abbott, Marilyn Walker, Pranav Anand, Jean E. Fox Tree, Robeson Bowmani, and Joseph King. 2011. How can you say such things? Recognizing disagreement in informal political argument. In Proceedings of the Workshop on LSM (LSM’11). Association for Computational Linguistics, 2--11.

Digital Library

[2]

Sinan Aral, Lev Muchnik, and Arun Sundararajan. 2009. Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc. Natl. Acad. Sci. U.S.A. 106, 51 (2009), 21544--21549.

[3]

Sinan Aral and Dylan Walker. 2012. Identifying influential and susceptible members of social networks. Science 337, 6092 (20 July 2012), 337--341.

[4]

Eytan Bakshy, Jake M. Hofman, Winter A. Mason, and Duncan J. Watts. 2011. Everyone’s an influencer: Quantifying influence on twitter. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (WSDM’11). ACM, New York, NY, 65--74.

Digital Library

[5]

R.F. Bales. 1969. Personality and Interpersonal Behavior. Holt, Rinehart, 8 Winston.

[6]

R. F. Bales, Strodtbeck, Mills F. L., T. M., and M. Roseborough. 1951. Channels of communication in small groups. Am. Sociol. Rev. (1951), 16(4), 461--468.

[7]

Nicola Barbieri, Francesco Bonchi, and Giuseppe Manco. 2013. Topic-aware social influence propagation models. Knowl. Inf. Syst. 37, 3 (2013), 555--584.

[8]

Or Biran and Owen Rambow. 2011a. Identifying justifications in written dialogs. In Proceedings of the 2011 IEEE 5th International Conference on Semantic Computing (ICSC’11). IEEE Computer Society, Washington, DC, 162--168.

Digital Library

[9]

Or Biran and Owen Rambow. 2011b. Identifying justifications in written dialogs by classifying text as argumentative. Int. J. Seman. Comput. 5, 4 (2011), 363--381.

[10]

Or Biran, Sara Rosenthal, Jacob Andreas, Kathleen McKeown, and Owen Rambow. 2012. Detecting influencers in written online conversations. In Proceedings of the Language in Social Media 2012 Workshop.

Digital Library

[11]

Konstantinos Bousmalis, Marc Mehu, and Maja Pantic. 2013. Towards the automatic detection of spontaneous agreement and disagreement based on nonverbal behaviour: A survey of related cues, databases, and tools. Image Vision Comput. 31, 2 (Feb. 2013), 203--221.

Digital Library

[12]

Jack W. Brehm. 1989. Psychological reactance: Theory and applications. Adv. Consum. Res. 16, 1 (1989), 72--75.

[13]

M.E. Brook and S. H. Ng. 1986. Language and social influence in small conversational groups. J. Lang. Soc. Psychol. (1986), 5(3), 201--210.

[14]

E. Cambria. 2016. Affective computing and sentiment analysis. IEEE Intell. Syst. 31, 2 (Mar 2016), 102--107.

Digital Library

[15]

Lynn Carlson, Daniel Marcu, and Mary Ellen Okurowski. 2003. Building a discourse-tagged corpus in the framework of rhetorical structure theory. In Current Directions in Discourse and Dialogue, Jan van Kuppevelt and Ronnie Smith (Eds.). Kluwer Academic Publishers.

Digital Library

[16]

Robert B. Cialdini. 2007. Influence: The Psychology of Persuasion (Collins Business Essentials) (revised ed.). Harper Paperbacks.

[17]

Cristian Danescu-Niculescu-Mizil, Lillian Lee, Bo Pang, and Jon Kleinberg. 2012. Echoes of power: Language effects and power differences in social interaction. In Proceedings of the 21st International Conference on WWW (WWW’12). ACM, New York, NY, 699--708.

Digital Library

[18]

Hal Daumé, III. 2007. Frustratingly easy domain adaptation. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. Association for Computational Linguistics, 256--263.

[19]

P. Alex Dow, Lada A. Adamic, and Adrien Friggeri. 2013. The anatomy of large facebook cascades. In ICWSM, Emre Kiciman, Nicole B. Ellison, Bernie Hogan, Paul Resnick, and Ian Soboroff (Eds.). The AAAI Press.

[20]

Richard Driscoll, Keith E. Davis, and Milton E. Lipetz. 1972. Parental interference and romantic love: The romeo and juliet effect. J. Person. Soc. Psychol. 24, 1 (1972), 1--10.

[21]

Emilio Ferrara. 2012. A large-scale community structure analysis in Facebook. EPJ Data Sci. 1, 1 (2012).

[22]

Michel Galley, Kathleen McKeown, Julia Hirschberg, and Elisabeth Shriberg. 2004. Identifying agreement and disagreement in conversational speech: Use of bayesian networks to model pragmatic dependencies. In Proceedings of the 42nd Meeting of the Association for Computational Linguistics (ACL’04).

Digital Library

[23]

Malcolm Gladwell. 2002. The Tipping Point: How Little Things Can Make a Big Difference. Back Bay Books.

[24]

Amit Goyal, Francesco Bonchi, and Laks V. S. Lakshmanan. 2011. A data-based approach to social influence maximization. Proc. VLDB Endow. 5, 1 (Sept. 2011), 73--84.

Digital Library

[25]

Weiwei Guo and Mona Diab. 2012. Modeling sentences in the latent space. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 864--872.

Digital Library

[26]

Junming Huang, Xue-Qi Cheng, Hua-Wei Shen, Tao Zhou, and Xiaolong Jin. 2012. Exploring social influence via posterior effect of word-of-mouth recommendations. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM’12). ACM, New York, NY, 573--582.

Digital Library

[27]

Adam Janin, Don Baron, Jane Edwards, Dan Ellis, David Gelbart, Nelson Morgan, Barbara Peskin, Thilo Pfau, Elizabeth Shriberg, Andreas Stolcke, and Chuck Wooters. 2003. The ICSI Meeting Corpus.

[28]

Elihu Katz and Paul F. Lazarsfeld. 1955. Personal Influence. Free Press, Glencoe, IL.

[29]

Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010. What is twitter, A social network or a news media? In Proceedings of the 19th International Conference on World Wide Web (WWW’10). ACM, New York, NY, 591--600.

Digital Library

[30]

Timothy La Fond and Jennifer Neville. 2010. Randomization tests for distinguishing social influence and homophily effects. In Proceedings of the 19th International Conference on World Wide Web (WWW’10). ACM, New York, NY, 601--610.

Digital Library

[31]

E. Langer. 1989. Minding matters the consequences of mindlessness-mindfulness. Adv. Exp. Soc. Psychol. 22 (1989), 137--173.

[32]

Arjun Mukherjee and Bing Liu. 2010. Improving gender classification of blog authors. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP’10). Association for Computational Linguistics, Stroudsburg, PA, 207--217.

Digital Library

[33]

Seth A. Myers, Chenguang Zhu, and Jure Leskovec. 2012. Information diffusion and external influence in networks. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). ACM, New York, NY, 33--41.

Digital Library

[34]

S. H. Ng, D. Bell, and M. Brooke. 1993. Gaining turns and achieving high in influence ranking in small conversational groups. Br. J. Soc. Psychol. 32, 265--275.

[35]

S. H. Ng, M. Brooke, and M. Dunne. 1995. Interruption and in influence in discussion groups. J. Lang. Soc. Psychol. 14, 4, 369--381.

[36]

Dong Nguyen, Noah A. Smith, and Carolyn P. Rosé. 2011. Author age prediction from text using linear regression. In Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH’11). Association for Computational Linguistics, Stroudsburg, PA, 115--123.

Digital Library

[37]

Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, Deborah Cai, Jennifer Midberry, and Yuanxin Wang. 2013. Modeling topic control to detect influence in conversations using nonparametric topic models. In Machine Learning. Springer, 1--41.

Digital Library

[38]

Eric W. Noreen. 1989. Computer-Intensive Methods for Testing Hypotheses: An Introduction. Wiley-Interscience.

[39]

Bo Pang and Lillian Lee. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd Annual Meeting on ACL (ACL’04). Association for Computational Linguistics, Stroudsburg, PA, Article 271.

Digital Library

[40]

David P. Phillips and Lundie L. Carstensen. 1988. The effect of suicide stories on various demographic groups, 1968--1985. Suicide Life-Threat. Behav. 18, 1 (1988), 100--114.

[41]

Barbara Plank and Gertjan van Noord. 2011. Effective measures of domain similarity for parsing. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. ACL, 1566--1576.

Digital Library

[42]

I. Poggi, F. D?Errico, and L. Vincze. 2010. Agreement and its multimodal communication in debates. A qualitative analysis. Cognitive Computation (Aug. 2010), 1--14.

[43]

Vinodkumar Prabhakaran and Owen Rambow. 2013. Written dialog and social power: Manifestations of different types of power in dialog behavior. In Proceedings of the 6th International Joint Conference on Natural Language Processing (IJCNLP’13). 216--224.

[44]

Vinodkumar Prabhakaran and Owen Rambow. 2014. Predicting power relations between participants in written dialog from a single thread. In Proceedings of the 52nd Annual Meeting of the ACL 2014, Baltimore, MD, USA, Volume 2: Short Papers. 339--344.

[45]

Vinodkumar Prabhakaran, Owen Rambow, and Mona Diab. 2010. Automatic committed belief tagging. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters (COLING’10). ACL, Stroudsburg, PA, 1014--1022.

Digital Library

[46]

Vinodkumar Prabhakaran, Emily E. Reid, and Owen Rambow. 2014. Gender and power: How gender and gender environment affect manifestations of power. In Proceedings of the 2014 EMNLP Conference. 1965--1976.

[47]

Daniele Quercia, Jonathan Ellis, Licia Capra, and Jon Crowcroft. 2011. In the mood for being influential on twitter. In SocialCom/PASSAT. IEEE, 307--314.

[48]

Scott A. Reid and Sik Hung Ng. 2000. Conversation as a resource for in influence: Evidence for prototypical arguments and social identification processes. Eur. J. Soc. Psychol. (2000), 30, 83--100.

[49]

Sara Rosenthal. 2015. Detecting Influencers in Social Media Discussions. Ph.D. Dissertation. Columbia University.

[50]

Sara Rosenthal and Kathleen McKeown. 2011. Age prediction in blogs: A study of style, content, and online behavior in pre- and post-social media generations. In Proceedings of ACL-HLT.

Digital Library

[51]

Sara Rosenthal and Kathleen McKeown. 2012. Detecting opinionated claims in online discussions. In Proceedings of the 2012 IEEE 6th International Conference on Semantic Computing Special Session on Semantics and Sociolinguistics in Social Media (ICSC’12). IEEE Computer Society.

Digital Library

[52]

Sara Rosenthal and Kathy McKeown. 2013. Columbia NLP: Sentiment detection of subjective phrases in social media. In Proceedings of the 2nd Joint Conference on Lexical and Computational Semantics (^*SEM), Volume 2: Proceedings of the 7th International Workshop on Semantic Evaluation (SemEval’13). ACL, 478--482.

[53]

Sara Rosenthal and Kathy McKeown. 2015. I couldn’t agree more: The role of conversational structure in agreement and disagreement detection in online discussions. In Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Association for Computational Linguistics, 168--177.

[54]

Sara Rosenthal, Kathy McKeown, and Apoorv Agarwal. 2014. Columbia NLP: Sentiment detection of sentences and subjective phrases in social media. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval’14). Association for Computational Linguistics and Dublin City University, 198--202.

[55]

K. R. Scherer. 1979. Voice and speech correlates of perceived social influence in simulated juries. In Language and Social Psychology, H. Giles and R. St Clair (Eds). Blackwell, Oxford, UK, 88--120.

[56]

J. Schler, M. Koppel, S. Argamon, and J. Pennebaker. 2006. Effects of age and gender on blogging. In AAAI Spring Symposium on Computational Approaches for Analyzing Weblogs.

[57]

Tomek Strzalkowski, Samira Shaikh, Ting Liu, George Aaron Broadwell, Jennifer Stromer-Galley, Sarah M. Taylor, Veena Ravishankar, Umit Boz, and Xiaoai Ren. 2013. Influence and power in group interactions. In SBP. 19--27.

Digital Library

[58]

Swabha Swayamdipta and Owen Rambow. 2012. The pursuit of power and its manifestation in written dialog. In ICSC. IEEE Computer Society, 22--29.

Digital Library

[59]

Jeffrey Travers, Stanley Milgram, Jeffrey Travers, and Stanley Milgram. 1969. An experimental study of the small world problem. Sociometry 32 (1969), 425--443.

[60]

D. J. Watts and P. S. Dodds. 2007. Influentials, networks, and public opinion formation. J. Consum. Res. 34 (2007), 441--458.

[61]

Shaomei Wu, Jake M. Hofman, Winter A. Mason, and Duncan J. Watts. 2011. Who says what to whom on twitter. In Proceedings of the 20th International Conference on World Wide Web (WWW’11). ACM, New York, NY, 705--714.

Digital Library

[62]

Alexander Yeh. 2000. More accurate tests for the statistical significance of result differences. In Proceedings of the 18th Conference on Computational Linguistics - Volume 2 (COLING’00). Association for Computational Linguistics, Stroudsburg, PA, 947--953.

Digital Library

[63]

Joel Young, Craig Martell, Pranav Anand, Pedro Ortiz, and Henry Tucker Gilbert, IV. 2011. A microtext corpus for persuasion detection in dialog. In Proceedings of the 5th AAAI Conference on Analyzing Microtext (AAAIWS’11-05). AAAI Press, 80--85.

Digital Library

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Detecting Influencers in Multiple Online Genres

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 17, Issue 2

Special Issue on Advances in Social Computing and Regular Papers

May 2017

249 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3068849

Editor:
Munindar P. Singh
Department of Computer Science, North Carolina State University

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Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 23 March 2017

Accepted: 01 October 2016

Revised: 01 October 2016

Received: 01 February 2016

Published in TOIT Volume 17, Issue 2

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