GroupRank: Ranking Online Social Groups Based on User Membership Records

Social networks are valuable sources for the marketers, who can publish campaigns to reach target audiences according to their interest. Although Telegram was primarily designed as an instant messenger, it is now used as a social network in Iran due to the censorship of Facebook, Twitter, etc. Telegram neither provides a marketing platform nor the possibility to search among groups. It is difficult for the marketers to find target audience groups in Telegram, and hence, we have developed a system to fill the gap. The 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 a detailed analysis, five group quality factors are introduced and used in the ranking. Our proposed method combines the TF-IDF scoring with group quality scores and associative connections among groups. The experimental results show improvement in many different queries.