Leveraging Followee List Memberships for Inferring User Interests for Passive Users on Twitter

User modeling for inferring user interests from Online Social Networks (OSNs) such as Twitter has received great attention in the user modeling community with the growing popularity of OSNs. The focus of previous works has been on analyzing user-generated content such as tweets to infer user interests. Therefore, these previous studies were limited to active users who have been actively generating content. On the other hand, with the percentage of passive use of OSNs on the rise, some researchers investigated different types of information about followees (i.e., people that a user is following) such as tweets, usernames, and biographies to infer user interests for passive users who use OSNs for consuming information from followees but who do not produce any content. Although different types of information about followees have been exploited, list memberships (a topical list which other Twitter users can freely add a user into) of followees have not yet been investigated extensively for inferring user interests. In this paper, we investigate list memberships of followees, to infer interest profiles for passive users. To this end, we propose user modeling strategies with two different weighting schemes as well as a refined interest propagation strategy based on previous work. In addition, we investigate whether the information from biographies and list memberships of followees can complement each other, and thus improve the quality of inferred interest profiles for passive users. Results show that leveraging list memberships of followees is useful for inferring user interests when the number of followees is relatively small compared to using biographies of followees. In addition, we found that combining the two different types of information (list memberships and biographies) of followees can improve the quality of user interest profiles significantly compared to a state-of-art method in the context of link recommendations on Twitter.

[1]  John G. Breslin,et al.  Aggregated, interoperable and multi-domain user profiles for the social web , 2012, I-SEMANTICS '12.

[2]  Mohsen Kahani,et al.  Semantics-Enabled User Interest Detection from Twitter , 2015, 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT).

[3]  Michael Granitzer,et al.  Inferring semantic interest profiles from Twitter followees: does Twitter know better than your friends? , 2016, SAC.

[4]  Barry Smyth,et al.  A multi-faceted user model for twitter , 2012, UMAP.

[5]  Gerard Salton,et al.  Research and Development in Information Retrieval , 1982, Lecture Notes in Computer Science.

[6]  Jens Lehmann,et al.  DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia , 2015, Semantic Web.

[7]  Stefano Faralli,et al.  Recommendation of microblog users based on hierarchical interest profiles , 2015, Social Network Analysis and Mining.

[8]  Fabio Crestani,et al.  Building user profiles from topic models for personalised search , 2013, CIKM.

[9]  Mirella Lapata,et al.  The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 19-24 June, 2011, Portland, Oregon, USA - Student Session , 2011, ACL.

[10]  Amit P. Sheth,et al.  Semantic Filtering for Social Data , 2016, IEEE Internet Computing.

[11]  Yang Liu,et al.  Interactive Group Suggesting for Twitter , 2011, ACL.

[12]  Ryen W. White,et al.  Predicting user interests from contextual information , 2009, SIGIR.

[13]  Patrick Siehndel,et al.  TwikiMe! - User Profiles That Make Sense , 2012, International Semantic Web Conference.

[14]  Hakan Ferhatosmanoglu,et al.  Short text classification in twitter to improve information filtering , 2010, SIGIR.

[15]  Peter Ingwersen,et al.  Polyrepresentation of information needs and semantic entities: elements of a cognitive theory for information retrieval interaction , 1994, SIGIR '94.

[16]  Jan Pedersen,et al.  Inferring User Interests From Microblogs , 2014 .

[17]  Pasquale Lops,et al.  Leveraging Encyclopedic Knowledge for Transparent and Serendipitous User Profiles , 2013, UMAP.

[18]  Qi Gao,et al.  Analyzing temporal dynamics in Twitter profiles for personalized recommendations in the social web , 2011, WebSci '11.

[19]  John G. Breslin,et al.  Analyzing Aggregated Semantics-enabled User Modeling on Google+ and Twitter for Personalized Link Recommendations , 2016, UMAP.

[20]  Krishna P. Gummadi,et al.  You are who you know: inferring user profiles in online social networks , 2010, WSDM '10.

[21]  Michael S. Bernstein,et al.  Short and tweet: experiments on recommending content from information streams , 2010, CHI.

[22]  Geert-Jan Houben,et al.  Leveraging User Modeling on the Social Web with Linked Data , 2012, ICWE.

[23]  John G. Breslin,et al.  Inferring User Interests for Passive Users on Twitter by Leveraging Followee Biographies , 2017, ECIR.

[24]  Amit P. Sheth,et al.  User Interests Identification on Twitter Using a Hierarchical Knowledge Base , 2014, ESWC.

[25]  Matthew Michelson,et al.  Tweet Disambiguate Entities Retrieve Folksonomy SubTree Step 1 : Discover Categories Generate Topic Profile from SubTrees Step 2 : Discover Profile Topic Profile : “ English Football ” “ World Cup ” , 2010 .

[26]  John G. Breslin,et al.  Interest Representation, Enrichment, Dynamics, and Propagation: A Study of the Synergetic Effect of Different User Modeling Dimensions for Personalized Recommendations on Twitter , 2016, EKAW.

[27]  John G. Breslin,et al.  Exploring Dynamics and Semantics of User Interests for User Modeling on Twitter for Link Recommendations , 2016, SEMANTiCS.

[28]  Michael Granitzer,et al.  On the quality of semantic interest profiles for onine social network consumers , 2016, SIAP.

[29]  Fattane Zarrinkalam.  Semantics-Enabled User Interest Mining , 2015, ESWC.

[30]  Marta Sabou,et al.  The Semantic Web. Latest Advances and New Domains , 2015, Lecture Notes in Computer Science.

[31]  Alexander J. Smola,et al.  Scalable distributed inference of dynamic user interests for behavioral targeting , 2011, KDD.

[32]  Qi Gao,et al.  Analyzing user modeling on twitter for personalized news recommendations , 2011, UMAP'11.

[33]  John G. Breslin,et al.  User Modeling on Twitter with WordNet Synsets and DBpedia Concepts for Personalized Recommendations , 2016, CIKM.

[34]  Alice Oh,et al.  Analysis of Twitter Lists as a Potential Source for Discovering Latent Characteristics of Users , 2010 .

[35]  Qi Gao,et al.  Semantic Enrichment of Twitter Posts for User Profile Construction on the Social Web , 2011, ESWC.