Thursday, August 01, 2019

Bot stamina: Examining the influence and staying power of bots in online social networks

Following on with our work on bots we just had a paper published in Applied Network Science entitled "Bot Stamina: Examining the Influence and Staying Power of Bots in Online Social Networks" which is significant extension of a previous conference paper. In the paper we look at thee global Twitter conversions in 2016. Specifically the 2016 U.S. presidential election primary races (February 1–28, 2016), the ongoing Ukrainian conflict (August 1–28, 2016), and the Turkish government’s implementation of censorship (December 1–28, 2016) and the influence of Bots on these conversations.

Tweets were classified as Bots using DeBot and then we explore the relative importance and persistence of social bots in online social networks by looking at retweet networks and centrality rankings (i.e. degree, in-degree, out-degree, eigenvector, betweenness and PageRank). We find through such centrality measurements that even though Bots made up less than 0.3% of the total user population, they displayed a profound level of structural network influence.  If you would like to know more about this work, below we provide the abstract to the paper, along with some figures, including one that describes our methodology, and some initial results. Finally at the bottom of the page we provide the full reference and a link to the paper.

Abstract
This study presents a novel approach to expand the emergent area of social bot research. We employ a methodological framework that aggregates and fuses data from multiple global Twitter conversations with available bot detection platforms and ultimately classifies the relative importance and persistence of social bots in online social networks (OSNs). In testing this methodology across three major global event OSN conversations in 2016, we confirmed the hyper-social nature of bots: suspected social bot accounts make far more attempts on average than social media accounts attributed to human users to initiate contact with other accounts via re-tweets. Social network analysis centrality measurements discover that social bots, while comprising less than 0.3% of the total corpus user population, display a dis-proportionately high profound level of structural network influence by ranking particularly high among the top users across multiple centrality measures within the OSN conversations of interest. Further, we show that social bots exhibit temporal persistence in centrality ranking density when examining these same OSN conversations over time.

Keywords: Social bot analysis, computational social science, social network analysis, online social networks




Full Reference:
Schuchard, R., Crooks, A.T., Stefanidis, A. and Croitoru, A. (2019), Bot Stamina: Examining the Influence and Staying Power of Bots in Online Social Networks, Applied Network Science, 4: 55. Available at https://doi.org/10.1007/s41109-019-0164-x. (pdf)