Benjamin D. Horne
Skip Abstract Section
Abstract
Knowing who is an expert on social media is a challenging yet important task, especially in a world where misleading information is commonplace and where social media is an important information source for knowledge seekers. In this paper we investigate expertise heuristics by comparing features of experts versus non-experts in big data settings. We employ a large set of features to classify experts and non-experts using data collected on two social media platform (Twitter and reddit). Our results show a good ability to predict who is an expert, especially using language-based features, validating that heuristics can be developed to differentiate experts from novices organically, based on social media use. Our results contribute to the development of expertise location and identification systems as well as our understanding on how experts present themselves on social media.
- Adamic, L. A., Zhang, J., Bakshy, E., & Ackerman, M. S. (2008). Knowledge sharing and Yahoo answers: Everyone knows something. In Proceedings of the 17th International Conference on World Wide Web, 665--674. Google Scholar
Digital Library
- Agichtein, E., Castillo, C., Donato, D., Gionis, A., & Mishne, G. (2008). Finding high-quality content in social media. In Proceedings of the 2008 international conference on web search and data mining, 183--194. Google ScholarDigital Library
- Alavi, M., & Leidner, D. E. (2001). Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 107--136. Google ScholarDigital Library
- Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011). Everyone's an influencer: Quantifying influence on Twitter. In Proceedings of the fourth ACM international conference on Web search and data mining, 65--74. Google ScholarDigital Library
- Balog, K., de Rijke, M., & Weerkamp, W. (2008). Bloggers as experts: Feed distillation using expert retrieval models. In Proceedings of SIGIR, 753--754. Google ScholarDigital Library
- Balthazard, P., Potter, R. E., & Warren, J. (2004). Expertise, extraversion and group interaction styles as performance indicators in virtual teams: how do perceptions of IT's performance get formed? ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 35(1), 41--64.? Google ScholarDigital Library
- Becerra-Fernandez, I. (2006). Searching for experts on the Web: A review of contemporary expertise locator systems. ACM Transactions on Internet Technology (TOIT), 6(4), 333--355.? Google ScholarDigital Library
- Buitinck L., Louppe G., Blondel M., Pedregosa F., Mueller A., Grisel O., Niculae V., Prettenhofer P., Gramfort A., Grobler J., Layton R., VanderPlas J., Joly A., Holt B., & Varoquaux G. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12(Oct), 2825--2830. Google ScholarDigital Library
- Chai, K., Potdar, V., & Dillon, T. (2009). Content quality assessment related frameworks for social media. Computational Science and Its Applications--ICCSA 2009, 791--805. Google ScholarDigital Library
- Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Journal of the American Society for Information Science, 50(2).Google Scholar
- Ehrlich, K., Lin, C. Y., & Griffiths-Fisher, V. (2007). Searching for experts in the enterprise: Combining text and social network analysis. In Proceedings of the 2007 International ACM Conference on Supporting Group Work, 117--126. Google ScholarDigital Library
- Faraj, S., & Sproull, L. (2000). Coordinating expertise in software development teams. Management Science, 46(12), 1554--1568.? Google ScholarDigital Library
- Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96--104. Google ScholarDigital Library
- Géron A. (2017). Hands on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media. Google ScholarDigital Library
- Guillaume L., Nogueira F., & Aridas C. (2017) Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning. Journal of Machine Learning Research, 1--5.Google Scholar
- Guy, I., Avraham, U., Carmel, D., Ur, S., Jacovi, M., & Ronen, I. (2013). Mining expertise and interests from social media. In Proceedings of WWW, 515--526. Google ScholarDigital Library
- Hessel, J., Tan, C., & Lee, L. (2016, December). Science, AskScience, and BadScience: On the Coexistence of Highly Related Communities. In ICWSM, 171--180.Google Scholar
- Higgins, M. (1999). Meta-information, and time: Factors in human decision making. Journal of the American Society for Information Science, 50(2). Google ScholarDigital Library
- Hirsch, J.E., (2005). An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 16569--16572.Google ScholarCross Ref
- Horne, B. D., & Adalı, S. (2017). The Impact of Crowds on News Engagement: A Reddit Case Study. In NECO at ICWSM.Google Scholar
- Horne, B. D., Nevo, D., Freitas, J., Ji, H., & Adalı, S. (2016). Expertise in social networks: How do experts differ from other users? In ICWSM, 583--586.Google Scholar
- Horne, B. D., Adalı, S., & Sikdar S. (2017). Identifying the social signals that drive online discussions: A case study of Reddit communities. In Proceeding of ICCCN.Google Scholar
- Hovland, C., Janis, I., & Kelly, H. (1953). Communication and persuasion; Psychological studies of opinion change. New Haven: Yale University Press.Google Scholar
- Jaech, A., Zayats, V., Fang, H., Ostendorf, M. & Hajishirzi, H. (2015). Talking to the crowd: What do people react to in online discussions?. arXiv preprint arXiv:1507.02205.Google Scholar
- Jurczyk, P., & Agichtein, E. (2007). Discovering authorities in question answer communities by using link analysis. In Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, 919--922. Google ScholarDigital Library
- Kardan, A., Garakani, M., & Bahrani, B. (2010). A method to automatically construct a user knowledge model in a forum environment. In Proceedings of SIGIR, 717--718. Google ScholarDigital Library
- Kintab, G. A., Roy, C. K., & McCalla, G. I. (2014). Recommending software experts using code similarity and social heuristics. In Proceedings of 24th Annual International Conference on Computer Science and Software Engineering, 4--18. Google ScholarDigital Library
- Korfiatis, N., GarcíA-Bariocanal, E., & Sánchez-Alonso, S. (2012). Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content. Electronic Commerce Research and Applications, 11(3), 205--217. Google ScholarDigital Library
- Kumar, S., Cheng, J., Leskovec, J., & Subrahmanian, V. S. (2017). An army of me: Sockpuppets in online discussion communities. In Proceedings of the 26th International Conference on World Wide Web, 857--866. Google ScholarDigital Library
- Li, C. T., & Shan, M. K. (2013). X2- search: Contextual expert search in social networks. In Proceedings of Conference on Technologies and Applications of Artificial Intelligence (TAAI), 176--181. Google ScholarDigital Library
- McPherson, M., Smith-Lovin, L. & Cook, J.M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 27(1), 415--444.Google Scholar
- Mun, Y. Y., Yoon, J. J., Davis, J. M., & Lee, T. (2013). Untangling the antecedents of initial trust in Web-based health information: The roles of argument quality, source expertise, and user perceptions of information quality and risk. Decision Support Systems, 55(1), 284--295. Google ScholarDigital Library
- Nevo, D., & Wand, Y. (2005). Organizational memory information systems: A transactive memory approach. Decision Support Systems, 39(4), 549--562.? Google ScholarDigital Library
- Nevo, D., Benbasat, I., & Wand, Y. (2012). Understanding technology support for organizational transactive memory: Requirements, application, and customization. Journal of Management Information Systems 28(4):69--98. Google ScholarDigital Library
- Page, R. (2012). The linguistics of self-branding and micro-celebrity in Twitter: The role of hashtags. Discourse & Communication, 6(2), 181--201.Google ScholarCross Ref
- Pal, A., Farzan, R., Konstan, J., & Kraut, R. (2011). Early detection of potential experts in question answering communities. User Modeling, Adaption and Personalization, 231--242. Google ScholarDigital Library
- Pennebaker, J.W., Chung, C.K., Frazee, J., Lavergne, G.M. & Beaver, D.I. (2014). When small words foretell academic success: The case of college admissions essays. PloS one, 9(12), p.e115844.Google ScholarCross Ref
- Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In In Communication and Persuasion. New York: Springer. 1--24.Google ScholarCross Ref
- Reuber, R. (1997). Management experience and management expertise. Decision Support Systems, 21(2), 51--60. Google ScholarDigital Library
- Riahi, F., Zolaktaf, Z., Shafiei, M., & Milios, E. (2012). Finding expert users in community question answering. In Proceedings of the 21st International Conference on World Wide Web, 791--798. Google ScholarDigital Library
- Shami, N. S., Ehrlich, K., Gay, G., & Hancock, J. T. (2009). Making sense of strangers' expertise from signals in digital artifacts. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 69--78. Google ScholarDigital Library
- Shanteau, J. (1992). The psychology of experts an alternative view. Expertise and decision support, 11--23.Google Scholar
- Shanteau, J., Weiss, D., Thomas, R., & Pounds, J. (2002). Performance-based assessment of expertise: How to decide if someone is an expert or not. European Journal of Operational Research 136(2):253--263.Google ScholarCross Ref
- Sikdar, S., Kang, B., O'Donovan, J., Hollerer, T., & Adalı, S. (2013). Understanding information credibility on Twitter. In Social computing (socialcom), 2013 international conference on, 19--24. Google ScholarDigital Library
- Starbird, K. (2017). Examining the Alternative Media Ecosystem Through the Production of Alternative Narratives of Mass Shooting Events on Twitter. In ICWSM, 230--239.Google Scholar
- Stiff, J. (1994). Persuasive communication. New York: The Guilford Press.Google Scholar
- Stewart, K. A., Baskerville, R., Storey, V. C., Senn, J. A., Raven, A., & Long, C. (2000). Confronting the assumptions underlying the management of knowledge: An agenda for understanding and investigating knowledge management. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 31(4), 41--53.? Google ScholarDigital Library
- Strong, D. M., Lee, Y. W., & Wang, R. Y. (1997). Data quality in context. Communications of the ACM, 40(5), 103--110.? Google ScholarDigital Library
- Tan, C., & Lee, L. (2015). All who wander: On the prevalence and characteristics of multi-community engagement. In Proceedings of the 24th International Conference on World Wide Web, 1056--1066. International World Wide Web Conferences Steering Committee. Google ScholarDigital Library
- Tausczik, Y. R., & Pennebaker, J. W. (2012). Participation in an online mathematics community: differentiating motivations to add. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work, 207--216. Google ScholarDigital Library
- Terveen, L., & Hill, W. (2001). Beyond recommender systems: Helping people help each other. HCI in the New Millennium, 1(2001), 487--509.Google Scholar
- Terveen, L., & McDonald, D. W. (2005). Social matching: A framework and research agenda. ACM transactions on computer-human interaction (TOCHI), 12(3), 401--434. Google ScholarDigital Library
- Trammell, K. D., & Keshelashvili, A. (2005). Examining the new influencers: A self-presentation study of A-list blogs. Journalism & Mass Communication Quarterly, 82(4), 968--982.Google ScholarCross Ref
- Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, New Series 185(4157):1124--1131.Google Scholar
- Unkelbach, C. (2007) Reversing the truth effect: Learning the interpretation of processing fluency in judgements of truth. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 219--230.Google ScholarCross Ref
- Varshney, K. R., Chenthamarakshan, V., Fancher, S. W., Wang, J., Fang, D., & Mojsilovi?, A. (2014). Predicting employee expertise for talent management in the enterprise. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 1729--1738. Google ScholarDigital Library
- Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5--33.? Google ScholarDigital Library
- Wang, G. A., Jiao, J., Abrahams, A. S., Fan, W., & Zhang, Z. (2013). ExpertRank: A topic-aware expert finding algorithm for online knowledge communities. Decision Support Systems, 54(3), 1442--1451. Google ScholarDigital Library
- Williams, C., & D'Mello, S. (2010). Predicting student knowledge level from domain-independent function and content words. In Intelligent tutoring systems, 62--71. Springer Berlin/Heidelberg. Google ScholarDigital Library
- Wu, S., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011, March). Who says what to whom on twitter. In Proceedings of the 20th international conference on World wide web, 705--714. Google ScholarDigital Library
- Yimam-Seid, D., & Kobsa, A. (2003). Expert-finding systems for organizations: Problem and domain analysis and the DEMOIR approach. Journal of Organizational Computing and Electronic Commerce, 13(1), 1--24.Google ScholarCross Ref
- Yogev, A., Guy, I., Ronen, I., Zwerdling, N., & Barnea, M. (2015). Social media-based expertise evidence. In Boulus-Rdje, N., Ellingsen, G., Bratteteig, T., Aanestad, M., & Bjrn, P., eds., ECSCW 2015: Proceedings of the 14th European Conference on Computer Supported Cooperative Work, 19--23 September 2015, Oslo, Norway. Springer International Publishing. 63--82.Google Scholar
- Zhang, J., & Ackerman, M. S. (2005). Searching for expertise in social networks: a simulation of potential strategies. In Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work, 71--80. Google ScholarDigital Library
- Zhang, J., Ackerman, M., & Adamic, L. (2007). Expertise networks in online communities: structure and algorithms. In Proceedings of WWW, 221--230. Google ScholarDigital Library
Index Terms
- Recognizing Experts on Social Media: A Heuristics-Based Approach
Recommendations
Mining expertise and interests from social media
WWW '13: Proceedings of the 22nd international conference on World Wide WebThe rising popularity of social media in the enterprise presents new opportunities for one of the organization's most important needs--expertise location. Social media data can be very useful for expertise mining due to the variety of existing ...
Uses and gratifications of social networking sites for bridging and bonding social capital
Applying uses and gratifications theory (UGT) and social capital theory, our study examined users of four social networking sites (SNSs) (Facebook, Twitter, Instagram, and Snapchat), and their influence on online bridging and bonding social capital. ...
Social capital, social media, and TV ratings
Motivated by the increasing role of social media in relating to economic outcomes, this paper examines the relationship between social networking sites SNS and television ratings drawing from the social capital theoretical framework of bonding and ...
Comments