Evolution of bot and human behavior during elections

2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), 2020

Social media platforms have taken political and cultural conversations to an online platform making them more accessible. Ability to anonymously post has allowed more people to participate fearlessly. However, this has also led to an opportunity to spread miss information and manipulative content. Political groups around the globe have used Bot accounts to help spread their preferred narrative online during elections. In the midst of 2019 Indian Lok Sabha Elections speculations were made about the presence of cyber-troops/IT Cells which operate fake accounts and push propaganda. Our finding suggests that a portion of Bot accounts seems to be operated by humans in the background. These accounts have a very distinct usage pattern on Twitter compared to legitimate human users. Our experiments also point out that only 1.3% of total interactions are directed from Humans to Bots, showing Bot accounts inability to gel well in the online social network.

Big Data

Computational propaganda deploys social or political bots to try to shape, steer, and manipulate online public discussions and influence decisions. Collective behavior of populations of social bots has not been yet widely studied, although understanding of collective patterns arising from interactions between bots would aid social bot detection. In this study, we show that there are significant differences in collective behavior between population of bots and population of humans as detected from their Twitter activity. Using a large dataset of tweets we have collected during the UK-EU referendum campaign, we separated users into population of bots and population of humans based on the length of sequences of their high-frequency tweeting activity. We show that, while pairwise correlations between users are weak, they co-exist with collective correlated states; however the statistics of correlations and co-spiking probability differ in both populations. Our results demonstrate that populations of social bots and human users in social media exhibit collective properties similar to the ones found in social and biological systems placed near a critical point.

arXiv (Cornell University), 2018

Serious concerns have been raised about the role of 'socialbots' in manipulating public opinion and influencing the outcome of elections by retweeting partisan content to increase its reach. Here we analyze the role and influence of socialbots on Twitter by determining how they contribute to retweet diffusions. We collect a large dataset of tweets during the 1st U.S. presidential debate in 2016 and we analyze its 1.5 million users from three perspectives: user influence, political behavior (partisanship and engagement) and botness. First, we define a measure of user influence based on the user's active contributions to information diffusions, i.e. their tweets and retweets. Given that Twitter does not expose the retweet structure -it associates all retweets with the original tweet -we model the latent diffusion structure using only tweet time and user features, and we implement a scalable novel approach to estimate influence over all possible unfoldings. Next, we use partisan hashtag analysis to quantify user political polarization and engagement. Finally, we use the BotOrNot API to measure user botness (the likelihood of being a bot). We build a two-dimensional "polarization map" that allows for a nuanced analysis of the interplay between botness, partisanship and influence. We find that not only are socialbots more active on Twitter -starting more retweet cascades and retweeting more -but they are 2.5 times more influential than humans, and more politically engaged. Moreover, pro-Republican bots are both more influential and more politically engaged than their pro-Democrat counterparts. However we caution against blanket statements that software designed to appear human dominates politics-related activity on Twitter. Firstly, it is known that accounts controlled by teams of humans (e.g. organizational accounts) are often identified as bots. Secondly, we find that many highly influential Twitter users are in fact pro-Democrat and that most pro-Republican users are mid-influential and likely to be human (low botness).

IEEE Transactions on Network and Service Management, 2020

While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election. Throughout our study, we classified involved users as social bots or humans, and examined their interactions from a quantitative (i.e., amount of traffic generated and existing relations) and qualitative (i.e., user's political affinity and sentiment towards the most important parties) perspectives. Results demonstrated that a non-negligible amount of those bots actively participated in the election, supporting each of the five principal political parties.

2024

The 2023 Ecuadorian presidential elections, contested between Daniel Noboa and Luisa González, marked a significant event in the nation's democratic journey. Amidst the fervor of campaigns and political debates, the role of social media, particularly Twitter, emerged as a focal point of interest. This study delves into the Twitter activities of both candidates, aiming to discern the potential use of automated actors, commonly known as "bots", in shaping online narratives and influencing public opinion. By employing state-of-the-art bot detection methodologies, we analyzed the tweet patterns, engagement metrics, and content dissemination strategies associated with the official accounts of the candidates. Our findings shed light on the extent and implications of bot-driven interactions, offering insights into the evolving dynamics of political communication in the digital age. The results underscore the importance of ensuring transparency and authenticity in online electoral campaigns, pivotal for preserving the sanctity of the democratic process.

The emergence of social bots within online social networks (OSNs) to diffuse information at scale has given rise to many efforts to detect them. While methodologies employed to detect the evolving sophistication of bots continue to improve, much work can be done to characterize the impact of bots on communication networks. In this study, we present a framework to describe the pervasiveness and relative importance of participants recognized as bots in various OSN conversations. Specifically, we harvested over 30 million tweets from three major global events in 2016 (the U.S. Presidential Election, the Ukrainian Conflict and Turkish Political Censorship) and compared the conversational patterns of bots and humans within each event. We further examined the social network structure of each conversation to determine if bots exhibited any particular network influence, while also determining bot participation in key emergent network communities. The results showed that although participants recognized as social bots comprised only 0.28% of all OSN users in this study, they accounted for a significantly large portion of prominent centrality rankings across the three conversations. This includes the identification of individual bots as top-10 influencer nodes out of a total corpus consisting of more than 2.8 million nodes.

Big Data, 2017

Automated and semiautomated Twitter accounts, bots, have recently gained significant public attention due to their potential interference in the political realm. In this study, we develop a methodology for detecting bots on Twitter using an ensemble of classifiers and apply it to study bot activity within political discussions in the Russian Twittersphere. We focus on the interval from February 2014 to December 2015, an especially consequential period in Russian politics. Among accounts actively Tweeting about Russian politics, we find that on the majority of days, the proportion of Tweets produced by bots exceeds 50%. We reveal bot characteristics that distinguish them from humans in this corpus, and find that the software platform used for Tweeting is among the best predictors of bots. Finally, we find suggestive evidence that one prominent activity that bots were involved in on Russian political Twitter is the spread of news stories and promotion of media who produce them.

Social Science Computer Review, 2021

The effectiveness of approaches to bot detection varies, with real-time detection being almost impossible. As a result, this article argues that the general Twitter using public cannot be expected to judge which accounts are bots with certainty and therefore do not know to what extent they are being manipulated online. In this article, the challenge of detecting bots and fake accounts is demonstrated by constructing two distinct methods to bot detection. The first method takes a fixed criteria-based approach, by building on commonly cited identifiers for bots. The second method takes a more flexible, investigative approach in order to uncover bots involved in coordinated efforts to influence online debates. As well as profiling the specific mechanics of how each one operates, we argue that they can be compared against an evaluative framework that specifies a set of key criteria that bot detection methods should meet in order to perform. Here, we identify four key criteria on which t...

2019

The increasing level of sophistication in the field of machine learning and artificial intelligence has engendered the creation of automated programs called "bots". Bots are created for varied reasons, and it has become imperative to evaluate the impact of bots on the social media ecosystem and consequently on our daily lives. However, despite the ubiquity of bots, very little research has been conducted to compare their trends and impacts on the social media ecosystem. To address this gap in bot research, we perform a comparative analysis of Facebook and Twitter bots, in terms of their popularity and impact on the society. This paper sets the foundation to allow for subsequent, more detailed studies into this subject area. Analyzing trends of these emerging technologies can provide insight into identifying their importance and roles in our everyday life. We provide a brief background of the subject, such as types of social bots and their utility, bot detection techniques, and the impact they have had on society. We then utilize the IBM Watson cognitive search and content analytics engine to examine the public perception of these bots. We also use the Google query volumes to investigate the trends of search terms related to Facebook and Twitter bots. Our findings suggest that there is a slightly higher public acceptance of Facebook bots as compared to Twitter bots. Furthermore, the utilization of bots on Online Social Networks (OSNs) is on the rise. Originally, bots were developed as a tool for driving user engagements on social media platforms. However, today bots are increasingly being used to convey mis/disinformation and political propaganda.

arXiv (Cornell University), 2020

A very large number of people use Online Social Networks daily. Such platforms thus become attractive targets for agents that seek to gain access to the attention of large audiences, and influence perceptions or opinions. Botnets, collections of automated accounts controlled by a single agent, are a common mechanism for exerting maximum influence. Botnets may be used to better infiltrate the social graph over time and to create an illusion of community behavior, amplifying their message and increasing persuasion. This paper investigates Twitter botnets, their behavior, their interaction with user communities and their evolution over time. We analyzed a dense crawl of a subset of Twitter traffic, amounting to nearly all interactions by Greek-speaking Twitter users for a period of 36 months. We detected over a million events where seemingly unrelated accounts tweeted nearly identical content at nearly the same time. We filtered these concurrent content injection events and detected a set of 1,850 accounts that repeatedly exhibit this pattern of behavior, suggesting that they are fully or in part controlled and orchestrated by the same software. We found botnets that appear for brief intervals and disappear, as well as botnets that evolve and grow, spanning the duration of our dataset. We analyze statistical differences between bot accounts and human users, as well as botnet interaction with user communities and Twitter trending topics.