Bot prediction on social networks of Twitter in altmetrics using deep graph convolutional networks

In the context of smart cities, it is crucial to filter out falsified information spread on social media channels through paid campaigns or bot-user accounts that significantly influence communication networks across the social communities and may affect smart decision-making by the citizens. In this paper, we focus on two major aspects of the Twitter social network associated with altmetrics: (a) to analyze the properties of bots on Twitter networks and (b) to distinguish between bots and human accounts. Firstly, we employed state-of-the-art social network analysis techniques that exploit Twitter's social network properties in novel altmetrics data. We found that 87% of tweets are affected by bots that are involved in the network's dominant communities. We also found that, to some extent, community size and the degree of distribution in Twitter's altmetrics network follow a power-law distribution. Furthermore, we applied a deep learning model, graph convolutional networks, to distinguish between organic (human) and bot Twitter accounts. The deployed model achieved the promising results, providing up to 71% classification accuracy over 200 epochs. Overall, the study concludes that bot presence in altmetrics-associated social media platforms can artificially inflate the number of social usage counts. As a result, special attention is required to eliminate such discrepancies when using altmetrics data for smart decision-making, such as research assessment either independently or complementary along with traditional bibliometric indices.