Social bots sour activist sentiment without eroding engagement

• URL: http://arxiv.org/abs/2403.12904v1
• Date: Tue, 19 Mar 2024 16:58:45 GMT
• Title: Social bots sour activist sentiment without eroding engagement
• Authors: Linda Li, Orsolya Vasarhelyi, Balazs Vedres,
• Abstract summary: We find that bots exert a greater influence on human behavior than vice versa during heated online periods. Political astroturfing bots increase activity, whereas other bots decrease it. Despite the seemingly minor impact of individual bot encounters, the cumulative effect is profound due to the large volume of bot communication.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social media platforms have witnessed a substantial increase in social bot activity, significantly affecting online discourse. Our study explores the dynamic nature of bot engagement related to Extinction Rebellion climate change protests from 18 November 2019 to 10 December 2019. We find that bots exert a greater influence on human behavior than vice versa during heated online periods. To assess the causal impact of human-bot communication, we compared communication histories between human users who directly interacted with bots and matched human users who did not. Our findings demonstrate a consistent negative impact of bot interactions on subsequent human sentiment, with exposed users displaying significantly more negative sentiment than their counterparts. Furthermore, the nature of bot interaction influences human tweeting activity and the sentiment towards protests. Political astroturfing bots increase activity, whereas other bots decrease it. Sentiment changes towards protests depend on the user's original support level, indicating targeted manipulation. However, bot interactions do not change activists' engagement towards protests. Despite the seemingly minor impact of individual bot encounters, the cumulative effect is profound due to the large volume of bot communication. Our findings underscore the importance of unrestricted access to social media data for studying the prevalence and influence of social bots, as with new technological advancements distinguishing between bots and humans becomes nearly impossible.

Related papers

• Sleeper Social Bots: a new generation of AI disinformation bots are already a political threat [0.0]
  "Sleeper social bots" are AI-driven social bots created to spread disinformation and manipulate public opinion. Preliminary findings suggest these bots can convincingly pass as human users, actively participate in conversations, and effectively disseminate disinformation. The implications of our research point to the significant challenges posed by social bots in the upcoming 2024 U.S. presidential election and beyond.
  arXiv  Detail & Related papers  (2024-08-07T19:57:10Z)
• My Brother Helps Me: Node Injection Based Adversarial Attack on Social Bot Detection [69.99192868521564]
  Social platforms such as Twitter are under siege from a multitude of fraudulent users. Due to the structure of social networks, the majority of methods are based on the graph neural network(GNN), which is susceptible to attacks. We propose a node injection-based adversarial attack method designed to deceive bot detection models.
  arXiv  Detail & Related papers  (2023-10-11T03:09:48Z)
• You are a Bot! -- Studying the Development of Bot Accusations on Twitter [1.7626250599622473]
  In the absence of ground truth data, researchers may want to tap into the wisdom of the crowd. Our research presents the first large-scale study of bot accusations on Twitter. It shows how the term bot became an instrument of dehumanization in social media conversations.
  arXiv  Detail & Related papers  (2023-02-01T16:09:11Z)
• Neural Generation Meets Real People: Building a Social, Informative Open-Domain Dialogue Agent [65.68144111226626]
  Chirpy Cardinal aims to be both informative and conversational. We let both the user and bot take turns driving the conversation. Chirpy Cardinal placed second out of nine bots in the Alexa Prize Socialbot Grand Challenge.
  arXiv  Detail & Related papers  (2022-07-25T09:57:23Z)
• Investigating the Validity of Botometer-based Social Bot Studies [0.0]
  Social bots are assumed to be automated social media accounts operated by malicious actors with the goal of manipulating public opinion. Social bot activity has been reported in many different political contexts, including the U.S. presidential elections. We point out a fundamental theoretical flaw in the widely-used study design for estimating the prevalence of social bots.
  arXiv  Detail & Related papers  (2022-07-23T09:31:30Z)
• Identification of Twitter Bots based on an Explainable ML Framework: the US 2020 Elections Case Study [72.61531092316092]
  This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
  arXiv  Detail & Related papers  (2021-12-08T14:12:24Z)
• BotNet Detection On Social Media [0.0]
  Social media has become an open playground for user (bot) accounts trying to manipulate other users using these platforms. There has been evidence of bots manipulating the election results which can be a great threat to the whole nation and hence the whole world. Our goal is to leverage semantic web mining techniques to identify fake bots or accounts involved in these activities.
  arXiv  Detail & Related papers  (2021-10-12T00:38:51Z)
• CheerBots: Chatbots toward Empathy and Emotionusing Reinforcement Learning [60.348822346249854]
  This study presents a framework whereby several empathetic chatbots are based on understanding users' implied feelings and replying empathetically for multiple dialogue turns. We call these chatbots CheerBots. CheerBots can be retrieval-based or generative-based and were finetuned by deep reinforcement learning. To respond in an empathetic way, we develop a simulating agent, a Conceptual Human Model, as aids for CheerBots in training with considerations on changes in user's emotional states in the future to arouse sympathy.
  arXiv  Detail & Related papers  (2021-10-08T07:44:47Z)
• Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn Chatbot Responding with Intention [55.77218465471519]
  This paper proposes an innovative framework to train chatbots to possess human-like intentions. Our framework included a guiding robot and an interlocutor model that plays the role of humans. We examined our framework using three experimental setups and evaluate the guiding robot with four different metrics to demonstrated flexibility and performance advantages.
  arXiv  Detail & Related papers  (2021-03-30T15:24:37Z)
• A Decade of Social Bot Detection [0.9137554315375922]
  In the aftermath of the 2016 US elections, the world started to realize the gravity of widespread deception in social media. What strategies should we enforce in order to stop this social bot pandemic? What stroke social, political and economic analysts after 2016, deception and automation, has been however a matter of study for computer scientists since at least 2010.
  arXiv  Detail & Related papers  (2020-06-23T13:46:38Z)
• Detection of Novel Social Bots by Ensembles of Specialized Classifiers [60.63582690037839]
  Malicious actors create inauthentic social media accounts controlled in part by algorithms, known as social bots, to disseminate misinformation and agitate online discussion. We show that different types of bots are characterized by different behavioral features. We propose a new supervised learning method that trains classifiers specialized for each class of bots and combines their decisions through the maximum rule.
  arXiv  Detail & Related papers  (2020-06-11T22:59:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.