GroupRank: Ranking Online Social Groups Based on User Membership Records

Abstract

Social networks are valuable sources for marketers. Marketers can publish campaigns to reach target audiences according to their interest. Although Telegram was primarily designed as an instant messenger, it is used as a social network in Iran due to censorship of Facebook, Twitter, etc. Telegram neither provides a marketing platform nor the possibility to search among groups. It is difficult for marketers to find target audience groups in Telegram, hence we developed a system to fill the gap. Marketers use our system to find target audience groups by keyword search. Our system has to search and rank groups as relevant as possible to the search query. This paper proposes a method called GroupRank to improve the ranking of group searching. GroupRank elicits associative connections among groups based on membership records they have in common. After detailed analysis, five-group quality factors have been introduced and used in the ranking. Our proposed method combines TF-IDF scoring with group quality scores and associative connections among groups. Experimental results show improvement in many different queries.