Follow-back Recommendations for Sports Bettors: A Twitter-based Approach

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dc.contributor.authorWandabwa, Herman
dc.contributor.authorNaeem, Muhammad Asif
dc.contributor.authorMirza, Farhaan
dc.contributor.authorPears, Russel
dc.date.accessioned2020-01-04T07:40:58Z
dc.date.available2020-01-04T07:40:58Z
dc.date.issued2020-01-07
dc.description.abstractSocial network based recommender systems are powered by a complex web of social discussions and user connections. Short text microblogs e.g. Twitter present powerful frameworks for information consumption, due to their real-time nature in content throughput as well as user connections. Therefore, users on such platforms consume the disseminated content to a greater or lesser extent based on their interests. Quantifying this degree of interest is a difficult task based on the amount of information that such platforms generate at any given time. Thus, the generation of personalized profiles based on the Degree of Interest (DoI) that users have towards certain topics in such short texts presents a research problem. We address this challenge by following a two-step process in generation of personalized sports betting related user profiles in tweets as a case study. We (i) compute the Degree of Interest in Sports Betting (DoiSB) of tweeters and (ii) affirm this DoiSB by correlating it with their friendship network. This is an integral process in the design of a short text based recommender systems for users to follow i.e follow-back recommendations as well as content-based recommendations relying on the interests of users on such platforms. In this paper, we described the DoiSB computation and follow-back recommendation process by building a vector representation model for tweets. We then use this model to profile users interested in sports betting. Experiments using real Twitter dataset geolocated to Kenya shows the effectiveness of our approach in the identification of tweeter's DoiSBs as well as their correlation with their friendship network.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.313
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/64055
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDecision Making in Online Social Networks
dc.subjectdata mining
dc.subjectinformation retrieval
dc.subjectsocial networks
dc.subjecttwitter
dc.subjectuser profiling
dc.titleFollow-back Recommendations for Sports Bettors: A Twitter-based Approach
dc.typeConference Paper
dc.type.dcmiText

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