A Novel Cross-Audience Analysis for Multi-Shared Content on Social Media
Abstract
Social media has gained popularity due to the existing technologies and advancements in internet and smartphone technologies. Recently, various regions have experienced different types of conflicts and wars. People in affected regions need real-time updates on these events. As it is mentioned earlier, social media has become a principal resource for such content. Multiple channels can be created across different platforms. For instance, a certain media organisation is capable of creating an account on different social media channels in a simple way. Users connected to these channels receive the same content but in a different structure and from different platforms. In this context, repetitive and redundant content delivered to users is necessary to deliver important content and check for the right one. This study aims to compare a set of metrics that highlights the interconnection between the audience community and the multiple accounts of media organisations that exist on social media. In particular, this study develops a specific framework that can detect the overlapping between interactive users across a media organisation channel on its pages on different social media platforms. In addition, this paper compares the sentiment analysis on these contents as well as the interactivity level and finally detects the hate speech of these contents. To address its proposition, this paper involved two popular social media platforms that are Facebook and X (Twitter). The results show interesting facts when comparing these metrics on the same posts.
Keywords: Audience analysis, social media, artificial intelligence, hate speech, source of information.
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