Leveraging expertise in news feeds: A Twitter case study
In EDA 2018, vol. RNTI-B-14, pp.1-16
Due to the large amount of information posted on social media, users find themselves overwhelmed by updates displayed chronologically in their news feed. Moreover, most of them are considered irrelevant. Ranking news feeds updates in order of relevance is proposed to help beneficiary users quickly catch up with the relevant updates. Four types of features are mainly used to predict the relevance: (1) the relevance of the update's content to the beneficiary's interests; (2) the social tie strength between the beneficiary and the update's author; (3) the author's authority; and (4) the update's quality. In this work, we propose an approach that leverages another type of feature which is the author's expertise for the update's topic. Experimental results on Twitter highlight that judging expertise is crucial for maximizing the relevance of updates in news feeds.