Social Media and AI

Source papers 33

The Arxiv papers behind this sub-topic. Links may take you off-site to arxiv.org.

• A Probabilistic Model for Using Social Networks in Personalized Item Recommendation
  “We first review previous research on using social networks to help recommend items to users. A crucial component of SPF is that it infers the influence that users have with each other. In previous wo…
• Are you in a Masquerade? Exploring the Behavior and Impact of Large Language Model Driven Social Bots in Online Social Networks
  As the capabilities of Large Language Models (LLMs) emerge, they not only assist in accomplishing traditional tasks within more efficient paradigms but also stimulate the evolution of social bots. Res…
• Attention on the brain
  Attention has a practical and specific online context. It has to do with managing the flood of data and advertisements vying for your time, which is finite. The metadata 'exhaust' (feeds, clicks, link…
• Characterizing Online Discussion Using Coarse Discourse Sequences
  As more social interaction takes place online, researchers have become interested in studying the discourse occurring in online social media. From these studies, researchers can examine how people con…
• Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems
  Many recommender systems are based on optimizing a linear weighting of different user behaviors, such as clicks, likes, shares, etc. Though the choice of weights can have a significant impact, there i…
• Conversations Gone Awry: Detecting Early Signs of Conversational Failure
  One of the main challenges online social systems face is the prevalence of antisocial behavior, such as harassment and personal attacks. In this work, we introduce the task of predicting from the very…
• Durably reducing conspiracy beliefs through dialogues with AI
  “Conspiracy theories are a paradigmatic example of beliefs that, once adopted, are extremely difficult to dispel. Influential psychological theories propose that conspiracy beliefs are uniquely resist…
• Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading
  In this study, we wish to showcase the unique utility of large language models (LLMs) in financial semantic annotation and alpha signal discovery. Leveraging a corpus of company-related tweets, we use…
• Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities
  method leverages the inherent vulnerabilities of LLMs in handling world knowledge, which can be exploited by attackers to unconsciously spread fabricated information. Through extensive experiments, we…
• Forecasting the presence and intensity of hostility on Instagram using linguistic and social features
  Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims…
• LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback
  Ensuring that online discussions are civil and productive is a major challenge for social media platforms. Such platforms usually rely both on users and on automated detection tools to flag inappropri…
• Large Language Models Can Infer Psychological Dispositions of Social Media Users
  Large Language Models (LLMs) demonstrate increasingly human-like abilities across a wide variety of tasks. In this paper, we investigate whether LLMs like ChatGPT can accurately infer the psychologica…
• Large Language Models For Social Networks: Applications, Challenges, And Solutions
  We categorize LLM applications for social networks into three categories. First is knowledge tasks where users want to find new knowledge and information, such as search and question-answering. Second…
• Linguistic markers of inherently false AI communication and intentionally false human communication: Evidence from hotel reviews
  To the human eye, AI-generated outputs of large language models have increasingly become indistinguishable from human-generated outputs. Therefore, to determine the linguistic properties that separate…
• Machine gaze in online behavioral targeting: The effects of algorithmic human likeness on social presence and social influence
  Digital platforms increasingly use online behavioral targeting (OBT) to enhance consumers’ engagement, which involves using algorithms to “gaze” at consumers—tracking their online activities and infer…
• Modeling Appropriate Language in Argumentation
  Online discussion moderators must make adhoc decisions about whether the contributions of discussion participants are appropriate or should be removed to maintain civility. Existing research on offens…
• News Source Citing Patterns in AI Search Systems
  We address this gap by analyzing data from the AI Search Arena, a head-to-head evaluation platform for AI search systems. The dataset comprises over 24,000 conversations and 65,000 responses from mode…
• Operating Multi-Client Influence Networks Across Platforms
  This report outlines several case studies on how actors have misused our models, as well as the steps we have taken to detect and counter such misuse. By sharing these insights, we hope to protect the…
• Posting versus Lurking: Communicating in a Multiple Audience Context
  “It is proposed that posters will be influenced by another’s opinion only when it is negative. Negative evaluators are seen as more intelligent, competent, and expert than positive evaluators (Amabile…
• Proactive Moderation of Online Discussions: Existing Practices and the Potential for Algorithmic Support
  Multiple studies on content moderation have identified a problem of scale: even if antisocial behavior is a small fraction of all content that gets posted, the sheer size of modern online platforms, t…
• Proxona: Leveraging LLM-Driven Personas to Enhance Creators' Understanding of Their Audience
  we present Proxona, a system for defining and extracting representative audience personas from the comments. Creators converse with personas to gain insights into their preferences and engagement, sol…
• Quantifying Controversy on Social Media
  Which topics spark the most heated debates on social media? Identifying those topics is not only interesting from a societal point of view, but also allows the filtering and aggregation of social medi…
• RLNVR: Reinforcement Learning from Non-Verified Real-World Rewards
  This paper introduces RLNVR (Reinforcement Learning from Non-Verified Rewards), a framework for training language models using noisy, real-world feedback signals without requiring explicit human verif…
• Real-time News Story Identification
  To improve the reading experience, many news sites organize news into topical collections, called stories. In this work, we present an approach for implementing real-time story identification for a ne…
• Recommender Systems with Social Regularization
  “Traditional recommender systems always ignore social relationships among users. But in our real life, when we are asking our friends for recommendations of nice digital cameras or touching movies, we…
• SMILE: Evaluation and Domain Adaptation for Social Media Language Understanding
  “Social media (SM) plays an increasingly important role in our lives. As of 2021, seven out of ten US adults use at least one social media platform like Facebook, Twitter, Instagram, or Pinterest [3].…
• Self-reinforcing cascades: A spreading model for beliefs or products of varying intensity or quality
  Models of how things spread often assume that transmission mechanisms are fixed over time. However, social contagions–the spread of ideas, beliefs, innovations–can lose or gain in momentum as they spr…
• Stance Detection on Social Media with Fine-Tuned Large Language Models
  The implementation of prompting strategies represents a significant departure from traditional NLP model training methods. By employing these strategies, LLMs can generate predictions without the exte…
• Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
  Synthetic data generation with Large Language Models (LLMs) has emerged as a promising paradigm for augmenting natural data over a nearly infinite range of tasks. However, most existing methods are fa…
• The impact of generative artificial intelligence on socioeconomic inequalities and policy making
  Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overvi…
• The persuasive effects of political microtargeting in the age of generative artificial intelligence
  The increasing availability of microtargeted advertising and the accessibility of generative artificial intelligence (AI) tools, such as ChatGPT, have raised concerns about the potential misuse of lar…
• Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy
  The widespread use of social media has led to a surge in popularity for automated methods of analyzing public opinion. Supervised methods are adept at text categorization, yet the dynamic nature of so…
• Web-Browsing LLMs Can Access Social Media Profiles and Infer User Demographics
  Large language models (LLMs) have traditionally relied on static training data, limiting their knowledge to fixed snapshots. Recent advancements, however, have equipped LLMs with web-browsing capabili…