Predicting user reactions to Twitter feed content based on personality type and social cues

Abstract

The events in the past few years clearly indicate that the modern social, political and economical landscapes are heavily influenced by how information flows through social networks. For instance, the recent outcomes of the US presidential elections and the Brexit vote show that misinformation and otherwise influencing content can affect events of great importance. In this paper, we adopt a simplified version of the recently proposed Network Knowledge Base (NKB) model to tackle the problem of predicting basic actions that a user can take given the content of their social media feeds: either take action (by reusing content seen in their feeds or creating new one), or otherwise take no action. We propose processing raw data obtained from social media based on the framework defined by the NKB model, and then formulate an action/no action prediction task that takes as input five features (including the user's personality type and other social cues), and then go on to show—via an extensive empirical evaluation with real-world Twitter data—that machine learning classification algorithms can be successfully applied in this setting to make predictions about user reactions. The main result obtained is that, out of the features considered, personality type based on the Big-5 (also known as OCEAN) model is the most impactful; furthermore, though the rest of the features taken individually do not have a significant impact, the best results are obtained when they are all taken together. This is a first step in applying the NKB model towards understanding the effect of pathogenic social media phenomena such as fake news, how they spread via cascades, and how to counteract their ill effects.