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Inferring User Interests for Passive Users on Twitter by Leveraging Followee Biographies

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Advances in Information Retrieval (ECIR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10193))

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Abstract

User modeling based on the user-generated content of users on social networks such as Twitter has been studied widely, and has been used to provide personalized recommendations via inferred user interest profiles. Most previous studies have focused on active users who actively post tweets, and the corresponding inferred user interest profiles are generated by analyzing these users’ tweets. However, there are also a great number of passive users who only consume information from Twitter but do not post any tweets. In this paper, we propose a user modeling approach using the biographies (i.e., self descriptions in Twitter profiles) of a user’s followees (i.e., the accounts that they follow) to infer user interest profiles for passive users. We evaluate our user modeling strategy in the context of a link recommender system on Twitter. Results show that exploring the biographies of a user’s followees improves the quality of user modeling significantly compared to two state-of-the-art approaches leveraging the names and tweets of followees.

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Notes

  1. 1.

    https://twitter.com/.

  2. 2.

    http://www.corporate-eye.com/main/facebooks-growing-problem-passive-users/.

  3. 3.

    http://guardianlv.com/2014/04/twitter-users-are-not-tweeting/.

  4. 4.

    https://www.wikipedia.org/.

  5. 5.

    https://support.twitter.com/articles/166337.

  6. 6.

    http://wibitaxonomy.org/.

  7. 7.

    https://bitbucket.org/beselch/interest_twitter_acmsac16.

  8. 8.

    http://aylien.com/.

  9. 9.

    The prefix dc denotes http://purl.org/dc/terms/.

  10. 10.

    The prefix dbo denotes http://dbpedia.org/ontology/.

  11. 11.

    https://dev.twitter.com/rest/public.

  12. 12.

    https://www.swarmapp.com.

  13. 13.

    http://www.sussex.ac.uk/its/pdfs/SPSS_Bootstrapping_22.pdf.

References

  1. Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Analyzing user modeling on twitter for personalized news recommendations. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 1–12. Springer, Heidelberg (2011). doi:10.1007/978-3-642-22362-4_1

    Chapter  Google Scholar 

  2. Abel, F., Hauff, C., Houben, G.-J., Tao, K.: Leveraging user modeling on the social web with linked data. In: Brambilla, M., Tokuda, T., Tolksdorf, R. (eds.) ICWE 2012. LNCS, vol. 7387, pp. 378–385. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31753-8_31

    Chapter  Google Scholar 

  3. Besel, C., Schlötterer, J., Granitzer, M.: Inferring semantic interest profiles from twitter followees: does twitter know better than your friends? In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, NY, USA, pp. 1152–1157. SAC 2016. ACM, New York (2016)

    Google Scholar 

  4. Chen, J., Nairn, R., Nelson, L., Bernstein, M., Chi, E.: Short and tweet: experiments on recommending content from information streams. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1185–1194. ACM (2010)

    Google Scholar 

  5. Faralli, S., Stilo, G., Velardi, P.: Recommendation of microblog users based on hierarchical interest profiles. Soc. Netw. Anal. Min. 5(1), 1–23 (2015)

    Article  Google Scholar 

  6. Flati, T., Vannella, D., Pasini, T., Navigli, R.: Two is bigger (and better) than one: the Wikipedia bitaxonomy project. In: ACL, vol. 1, pp. 945–955 (2014)

    Google Scholar 

  7. Kapanipathi, P., Jain, P., Venkataramani, C., Sheth, A.: User interests identification on twitter using a hierarchical knowledge base. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 99–113. Springer, Heidelberg (2014). doi:10.1007/978-3-319-07443-6_8

    Chapter  Google Scholar 

  8. Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S.: DBpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web J. 6(2), 167–195 (2015)

    Google Scholar 

  9. Michelson, M., Macskassy, S.A.: Discovering users’ topics of interest on twitter: a first look. In: Proceedings of the fourth workshop on Analytics for noisy unstructured text data, pp. 73–80. ACM (2010)

    Google Scholar 

  10. Mislove, A., Viswanath, B., Gummadi, K.P., Druschel, P.: You are who you know: inferring user profiles in online social networks. In: Proceedings of the third ACM international conference on Web search and data mining, pp. 251–260. ACM (2010)

    Google Scholar 

  11. Orlandi, F., Breslin, J., Passant, A.: Aggregated, interoperable and multi-domain user profiles for the social web. In: Proceedings of the 8th International Conference on Semantic Systems, pp. 41–48. ACM (2012)

    Google Scholar 

  12. Piao, G.: User modeling on twitter with WordNet Synsets and DBpedia concepts for personalized recommendations. In: The 25th ACM International Conference on Information and Knowledge Management. ACM (2016)

    Google Scholar 

  13. Piao, G., Breslin, J.G.: Analyzing aggregated semantics-enabled user modeling on Google+ and twitter for personalized link recommendations. In: User Modeling, Adaptation, and Personalization, pp. 105–109. ACM (2016)

    Google Scholar 

  14. Piao, G., Breslin, J.G.: Exploring dynamics and semantics of user interests for user modeling on twitter for link recommendations. In: 12th International Conference on Semantic Systems, pp. 81–88. ACM (2016)

    Google Scholar 

  15. Sheth, A., Kapanipathi, P.: Semantic filtering for social data. IEEE Internet Comput. 20(4), 74–78 (2016)

    Article  Google Scholar 

  16. Siehndel, P., Kawase, R.: TwikiMe!: user profiles that make sense. In: Proceedings of the 2012th International Conference on Posters and Demonstrations Track-Volume 914, pp. 61–64. CEUR-WS.org (2012)

    Google Scholar 

  17. Zarrinkalam, F., Fani, H., Bagheri, E., Kahani, M.: Inferring implicit topical interests on Twitter. In: Ferro, N., Crestani, F., Moens, M.-F., Mothe, J., Silvestri, F., Nunzio, G.M., Hauff, C., Silvello, G. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 479–491. Springer, Heidelberg (2016). doi:10.1007/978-3-319-30671-1_35

    Chapter  Google Scholar 

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Acknowledgments

This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (Insight Centre for Data Analytics).

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Authors and Affiliations

  1. Insight Centre for Data Analytics, National University of Ireland Galway, IDA Business Park, Lower Dangan, Galway, Ireland

    Guangyuan Piao & John G. Breslin

Authors
  1. Guangyuan Piao
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  2. John G. Breslin
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Corresponding author

Correspondence to Guangyuan Piao .

Editor information

Editors and Affiliations

  1. University of Glasgow , Glasgow, United Kingdom

    Joemon M Jose

  2. TU Delft - EWI/ST/WIS , Delft, The Netherlands

    Claudia Hauff

  3. Middle East Technical University , Ankara, Turkey

    Ismail Sengor Altıngovde

  4. Open University , Milton Keynes, United Kingdom

    Dawei Song

  5. Signal Media , London, United Kingdom

    Dyaa Albakour

  6. Toronto, Canada

    Stuart Watt

  7. JohnTait.net Ltd. and BCS IRSG , Sunderland, United Kingdom

    John Tait

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Piao, G., Breslin, J.G. (2017). Inferring User Interests for Passive Users on Twitter by Leveraging Followee Biographies. In: Jose, J., et al. Advances in Information Retrieval. ECIR 2017. Lecture Notes in Computer Science(), vol 10193. Springer, Cham. https://doi.org/10.1007/978-3-319-56608-5_10

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  • DOI: https://doi.org/10.1007/978-3-319-56608-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56607-8

  • Online ISBN: 978-3-319-56608-5

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