Society's reliance on social media as a primary source of news has spawned a renewed focus on the spread of misinformation. In this work, we identify the differences in how social media accounts identified as bots react to news sources of varying credibility, regardless of the veracity of the content those sources have shared. We analyze bot and human responses annotated using a fine-grained model that labels responses as being an answer, appreciation, agreement, disagreement, an elaboration, humor, or a negative reaction. We present key findings of our analysis into the prevalence of bots, the variety and speed of bot and human reactions, and the disparity in authorship of reaction tweets between these two sub-populations. We observe that bots are responsible for 9-15 of the reactions to sources of any given type but comprise only 7-10 of accounts responsible for reaction-tweets; trusted news sources have the highest proportion of humans who reacted; bots respond with significantly shorter delays than humans when posting answer-reactions in response to sources identified as propaganda. Finally, we report significantly different inequality levels in reaction rates for accounts identified as bots vs not.
Previous studies have identified the widespread presence of automated accounts or bots'' on social media. A 2014 filing from Twitter acknowledged that 8.5 percent of its active monthly users were automated accounts https: www.sec.gov Archives edgar data 1418091 000156459014003474 twtr-10q .htm? =1.155500795.1900968760.1407851022 and subsequent studies found this to be a low estimate of the actual prevalence of bot accounts @cite_14 @cite_12 . Recent work has found that accounts spreading disinformation are significantly more likely to be automated accounts @cite_15 . Other studies highlight evidence of bot participation in political discussion @cite_20 @cite_10 @cite_2 and astroturf campaigns that present the appearance of widespread support of a candidate, opinion, or topic artificially @cite_19 . A 2018 Pew Research center study found that the majority (66
@cite_14 @cite_19 @cite_2 @cite_15 @cite_10 @cite_12 @cite_20
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Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter. We leverage more than a thousand features extracted from public data and meta-data about users: friends, tweet content and sentiment, network patterns, and activity time series. We benchmark the classification framework by using a publicly available dataset of Twitter bots. This training data is enriched by a manually annotated collection of active Twitter users that include both humans and bots of varying sophistication. Our models yield high accuracy and agreement with each other and can detect bots of different nature. Our estimates suggest that between 9 and 15 of active Twitter accounts are bots. Characterizing ties among accounts, we observe that simple bots tend to interact with bots that exhibit more human-like behaviors. Analysis of content flows reveals retweet and mention strategies adopted by bots to interact with different target groups. Using clustering analysis, we characterize several subclasses of accounts, including spammers, self promoters, and accounts that post content from connected applications. We study astroturf political campaigns on microblogging platforms: politically-motivated individuals and organizations that use multiple centrally-controlled accounts to create the appearance of widespread support for a candidate or opinion. We describe a machine learning framework that combines topological, content-based and crowdsourced features of information diffusion networks on Twitter to detect the early stages of viral spreading of political misinformation. We present promising preliminary results with better than 96 accuracy in the detection of astroturf content in the run-up to the 2010 U.S. midterm elections. In the United States, social media sites—such as Facebook, Twitter, and YouTube—are currently being used by two out of three people ( 1 ), and search engines are used daily ( 2 ). Monitoring what users share or search for in social media and on the Web has led to greater insights into what people care about or pay attention to at any moment in time. Furthermore, it is also helping segments of the world population to be informed, to organize, and to react rapidly. However, social media and search results can be readily manipulated, which is something that has been underappreciated by the press and the general public. The massive spread of digital misinformation has been identified as a major threat to democracies. Communication, cognitive, social, and computer scientists are studying the complex causes for the viral diffusion of misinformation, while online platforms are beginning to deploy countermeasures. Little systematic, data-based evidence has been published to guide these efforts. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017. We find evidence that social bots played a disproportionate role in spreading articles from low-credibility sources. Bots amplify such content in the early spreading moments, before an article goes viral. They also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, resharing content posted by bots. Successful low-credibility sources are heavily supported by social bots. These results suggest that curbing social bots may be an effective strategy for mitigating the spread of online misinformation. Over the last several years political actors worldwide have begun harnessing the digital power of social bots — software programs designed to mimic human social media users on platforms like Facebook, Twitter, and Reddit. Increasingly, politicians, militaries, and government-contracted firms use these automated actors in online attempts to manipulate public opinion and disrupt organizational communication. Politicized social bots — here ‘political bots’ — are used to massively boost politicians’ follower levels on social media sites in attempts to generate false impressions of popularity. They are programmed to actively and automatically flood news streams with spam during political crises, elections, and conflicts in order to interrupt the efforts of activists and political dissidents who publicize and organize online. They are used by regimes to send out sophisticated computational propaganda. This paper conducts a content analysis of available media articles on political bots in order to build an event dataset of global political bot deployment that codes for usage, capability, and history. This information is then analyzed, generating a global outline of this phenomenon. This outline seeks to explain the variety of political bot-oriented strategies and presents details crucial to building understandings of these automated software actors in the humanities, social and computer sciences. Twitter is a new web application playing dual roles of online social networking and micro-blogging. Users communicate with each other by publishing text-based posts. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots, which appear to be a double-edged sword to Twitter. Legitimate bots generate a large amount of benign tweets delivering news and updating feeds, while malicious bots spread spam or malicious contents. More interestingly, in the middle between human and bot, there has emerged cyborg referred to either bot-assisted human or human-assisted bot. To assist human users in identifying who they are interacting with, this paper focuses on the classification of human, bot and cyborg accounts on Twitter. We first conduct a set of large-scale measurements with a collection of over 500,000 accounts. We observe the difference among human, bot and cyborg in terms of tweeting behavior, tweet content, and account properties. Based on the measurement results, we propose a classification system that includes the following four parts: (1) an entropy-based component, (2) a machine-learning-based component, (3) an account properties component, and (4) a decision maker. It uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot or cyborg. Our experimental evaluation demonstrates the efficacy of the proposed classification system. Bots are social media accounts that automate interaction with other users, and they are active on the StrongerIn-Brexit conversation happening over Twitter. These automated scripts generate content through these platforms and then interact with people. Political bots are automated accounts that are particularly active on public policy issues, elections, and political crises. In this preliminary study on the use of political bots during the UK referendum on EU membership, we analyze the tweeting patterns for both human users and bots. We find that political bots have a small but strategic role in the referendum conversations: (1) the family of hashtags associated with the argument for leaving the EU dominates, (2) different perspectives on the issue utilize different levels of automation, and (3) less than 1 percent of sampled accounts generate almost a third of all the messages.