Identifying the Topic-Specific Influential Users Using SLM

Abstract

Social Influence can be described as the ability to have an effect on the thoughts or actions of others. The objective of this research is to investigate the use of language in detecting the influential users in a specific topic on Twitter. From a collection of tweets matching a specified query, we want to detect the influential users from the tweets' text. The study investigates the Arabic Egyptian dialect and if it can be used for detecting the author's influence. Using a Statistical Language Model, we found a correlation between the users' average Retweets counts and their tweets' perplexity, consolidating the hypothesis that SLM can be trained to detect the highly retweeted tweets. However, the use of the perplexity for identifying influential users resulted in low precision values. The simplistic approach carried out did not produce good results. There is still work to be done for the SLM to be used for identifying influential users.