716 research outputs found
Bots in sozialen Netzwerken (Prof. Dr. Michael Mäs)
No full textProf. Dr. Michael Mäs (Institut für Technikzukünfte, ITZ) spricht über Bots in Sozialen Netzwerken.
Der Science Slam-Vortrag fand statt im Rahmen der Reihe „KIT im Rathaus“ am 18. Juli 2022. In der Reihe präsentierten Wissenschaftlerinnen und Wissenschaftler des KIT-Zentrum Mensch und Technik ihre Arbeit und aktuelle Forschungsprojekte.
Moderation: Philipp Schrögel (CAPAS, Universität Heidelberg
Guidelines for Developing Bots for GitHub
No full textProjects on GitHub rely on the automation provided by software development bots. Nevertheless, the presence of bots can be annoying and disruptive to the community. Backed by multiple studies with practitioners, this article provides guidelines for developing and maintaining software bots.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Software Engineerin
Human Aided Bots
No full textA chatbot is an example of a text-based conversational agent. While natural language understanding and machine learning techniques advance rapidly, current fully automated chatbots still struggle to serve their users well. Human intelligence, brought by crowd workers, freelancers or even full-time employees can be embodied in the chatbot logic to fill the gaps caused by limitations of fully automated solutions. In this paper we investigate Human Aided Bots, i.e. bots (including chatbots) using humans in the loop to operate. We survey industrial and academic examples of human aided bots, discuss their differences and common patterns, and identify open research questions.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information System
Bots, Botnet, and Misinformation: A Study
No full textabstract: Bots and networks of bots (known as a botnet) are a powerful tool in the world of misinformation. However, there are methods being developed to counter these tools. One such method is the use of Artificial Intelligence and machine learning to automatically filter, block, and identify bot accounts and bot posts. Since the influx of bot posts and videos is too much for hired people to handle in any way that is financially reasonable for a company, AI can be the key to preventing the spread of information
Types of Bots: Categorization of Accounts Using Unsupervised Machine Learning
No full textabstract: Social media bot detection has been a signature challenge in recent years in online social networks. Many scholars agree that the bot detection problem has become an "arms race" between malicious actors, who seek to create bots to influence opinion on these networks, and the social media platforms to remove these accounts. Despite this acknowledged issue, bot presence continues to remain on social media networks. So, it has now become necessary to monitor different bots over time to identify changes in their activities or domain. Since monitoring individual accounts is not feasible, because the bots may get suspended or deleted, bots should be observed in smaller groups, based on their characteristics, as types. Yet, most of the existing research on social media bot detection is focused on labeling bot accounts by only distinguishing them from human accounts and may ignore differences between individual bot accounts. The consideration of these bots' types may be the best solution for researchers and social media companies alike as it is in both of their best interests to study these types separately. However, up until this point, bot categorization has only been theorized or done manually. Thus, the goal of this research is to automate this process of grouping bots by their respective types. To accomplish this goal, the author experimentally demonstrates that it is possible to use unsupervised machine learning to categorize bots into types based on the proposed typology by creating an aggregated dataset, subsequent to determining that the accounts within are bots, and utilizing an existing typology for bots. Having the ability to differentiate between types of bots automatically will allow social media experts to analyze bot activity, from a new perspective, on a more granular level. This way, researchers can identify patterns related to a given bot type's behaviors over time and determine if certain detection methods are more viable for that type.Dissertation/ThesisPresentation Materials for Thesis DefenseMasters Thesis Computer Science 201
The strength of weak bots
No full textSome fear that social bots, automated accounts on online social networks, propagate falsehoods that can harm public opinion formation and democratic decision-making. Empirical research, however, resulted in puzzling findings. On the one hand, the content emitted by bots tends to spread very quickly in the networks. On the other hand, it turned out that bots’ ability to contact human users tends to be very limited. Here we analyze an agent-based model of social influence in networks explaining this inconsistency. We show that bots may be successful in spreading falsehoods not despite their limited direct impact on human users, but because of this limitation. Our model suggests that bots with limited direct impact on humans may be more and not less effective in spreading their views in the social network, because their direct contacts keep exerting influence on users that the bot does not reach directly. Highly active and well-connected bots, in contrast, may have a strong impact on their direct contacts, but these contacts grow too dissimilar from their network neighbors to further spread the bot’s content. To demonstrate this effect, we included bots in Axelrod’s seminal model of the dissemination of cultures and conducted simulation experiments demonstrating the strength of weak bots. A series of sensitivity analyses show that the finding is robust, in particular when the model is tailored to the context of online social networks. We discuss implications for future empirical research and developers of approaches to detect bots and misinformation
Survey of political bots on Twitter
No full textBots are software applications that execute automated tasks called scripts over the Internet. Bots have become predominant on social media platforms like Twitter, and automate their interactions with other users. Political Twitter bots have emerged that focus their activity on elections, policy issues, and political crises. These political bots have faced increased scrutiny as a result of their association with online manipulation via the spread of misinformation. As bots have become more sophisticated, research has focused on advanced methods to detect their presence on social media platforms. However, little research has been performed on the overall presence of political bots and their dynamic response to political events. The research that has been performed on political bots focuses on these bots in the context of scheduled political events, such as elections. In this paper, we explore the bot response to an unexpected political event, describe the overall presence of political bots on Twitter, and design and employ a model to identify them based on their user profile alone. We collected data for more than 700,000 accounts tweeting with hashtags related to political events in the United States between May 2018 and October 2018. We designed a machine learning algorithm using user profile features alone that achieves approximately 97.4% accuracy in identifying political Twitter bots. In our analysis, we found (1) new bot accounts are created in response to political events, (2) bot accounts are more active during political controversies, (3) the number of tweets an account has favorited (liked) is a strong determinant of bot status.M.S.Includes bibliographical referencesby David Troup
Product searching with shopping bots
No full textShopping bots are an important new innovation which support consumers with the product search and identification stage in e-shopping. This paper reviews the search facilities offered by shopping bots. A number of shopping bots that include books in their product range, have been visited with a view to analysing their search facilities. Using trial searches for three different best-selling books, title, author, and keyword search facilities available in a number of bots were further investigated. Finally the output from the search in terms of the number of items, and suppliers identified, and the price, was analysed. The effectiveness of bots does not only depend upon search facilities but also depends upon product coverage, and other added value features such as publisher and consumer reviews. Consumer search behaviour, in general, and the way in which consumers will use shopping bots are fruitful areas for further research
Categorizing and Discovering Social Bots
No full textabstract: Bots tamper with social media networks by artificially inflating the popularity of certain topics. In this paper, we define what a bot is, we detail different motivations for bots, we describe previous work in bot detection and observation, and then we perform bot detection of our own. For our bot detection, we are interested in bots on Twitter that tweet Arabic extremist-like phrases. A testing dataset is collected using the honeypot method, and five different heuristics are measured for their effectiveness in detecting bots. The model underperformed, but we have laid the ground-work for a vastly untapped focus on bot detection: extremist ideal diffusion through bots
Statuses most retweeted by pro-Trump bots.
No full textAutomated social media accounts, known as bots, have been shown to spread disinformation and manipulate online discussions. We study the behavior of retweet bots on Twitter during the first impeachment of U.S. President Donald Trump. We collect over 67.7 million impeachment related tweets from 3.6 million users, along with their 53.6 million edge follower network. We find although bots represent 1% of all users, they generate over 31% of all impeachment related tweets. We also find bots share more disinformation, but use less toxic language than other users. Among supporters of the Qanon conspiracy theory, a popular disinformation campaign, bots have a prevalence near 10%. The follower network of Qanon supporters exhibits a hierarchical structure, with bots acting as central hubs surrounded by isolated humans. We quantify bot impact using the generalized harmonic influence centrality measure. We find there are a greater number of pro-Trump bots, but on a per bot basis, anti-Trump and pro-Trump bots have similar impact, while Qanon bots have less impact. This lower impact is due to the homophily of the Qanon follower network, suggesting this disinformation is spread mostly within online echo-chambers.</div
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