A Prediction Model for Information Diffusion in Online Social Network

Abstract

The emergence of online social networks has brought many new platforms, e.g., Facebook, Flickr, YouTube, Sina Microblog, LinkedIn, and Twitter. These platforms are imperative constituents within the diffusion of information at an expansive scale, and Twitter is among the foremost utilized microblogging and online social organizing administrations. In Twitter, a title, phrase, or point tweeted at a greater rate than others are called a "trending topic" or "trend," and it becomes imperative to make available ways to evaluate this phenomenon. Assessing information diffusion appears to be an unsolvable perplex as these "trending topics" constitute a flood of views, thoughts, recommendations, considerations, proposals, etc., produced by users of these social networks. This paper thoroughly examined Twitter's trending topics in September 2019. We accessed Twitter's trends API for the month's trending topics and concocted six criteria to assess the dataset. These six criteria are location, lexical analysis, trending time, tweet volume, promo/giveaway, and social media influencer. Based on the criteria earlier mentioned, a prediction model was developed based on these criteria. Their results were used to predict how a piece of information would diffuse on the Twitter platform. General Terms Simulation, modeling