Misinformation on social media undermines informed discourse, yet most detection systems overlook how people actually judge credibility. This research bridges human perception and linguistic construction through two complementary analyses: large-scale measurement of user believability judgments in real social-media news discussions, and content-level modeling of linguistic ambivalence, the co-occurrence of conflicting emotional, cognitive, and temporal cues. Results show that perceived believability varies systematically with textual (verbs, proper nouns, interrogatives) and user features (personality proxies, writing complexity, emotional tone). Incorporating these explicit believability signals as edge features in graph neural networks substantially improves detection over content-only baselines. Complementary analyses reveal that domain specific ambivalence patterns—emotion–cognition tension in politics, sentiment mixing in entertainment, and present–future framing in health—serve as linguistic markers of deception. Together, these findings show that mitigating misinformation requires modeling both human credibility judgments and the linguistic conflicts that exploit them.

Large language models (LLMs) have dramatically reduced the cost and hardships of producing fluent and persuasive text, a capability that can also be repurposed to generate more or less convincing news. In this paper, we examine how subtle edits infused with LLM can systematically change the perceived believability of a news item. We introduce, for the first time, LLM-crafted targeted, low-visibility transformations to political news stories, preserving the core writing input, while altering credibility cues, to measure how adversarial changes shift human perception of believability. In contrast to prior work on fully synthetic forgeries or blunt paraphrases, our adversarial attack scenarios focus on subtle, context-aware credibility drift, involving small edits that alter causal assertions, remove qualifying facts, or introduce opposing evidence. These minimal modifications not only evade casual human scrutiny as reported in the literature, but also produce measurable shifts in perceived credibility as we show in our experimental evaluation.

This study expands the understanding of linguistic cues in fake news by exploring ambivalent language's role in distinguishing real from fake news and its impact on news spread. Unlike prior research focusing on positive or negative language separately, this work hypothesizes that fake news may exhibit higher ambivalence, aligning with its association with high-arousal emotions and writers' efforts to attract attention. Ambivalence, traditionally viewed as the co-occurrence of conflicting positive and negative elements, is extended here to include diverse dimensions such as textual, temporal, psychological, and content-related ambivalence.

Social media have become essential in daily life, serving as platforms for public opinion, personal growth, and news consumption. This shift in how people access news has led to an increase in misinformation, including fake news intended to deceive. Various psychological and social factors, such as emotional appeal, cognitive biases, and social influence, drive individuals' susceptibility to believing false information. While prior studies have examined the believability of news content, large-scale analyses on real social media platforms remain limited, as well as studying factors influencing the believability of real and fake news separately and analyzing similarities and differences. This study introduces a new dataset of 14,535 Twitter user comments, annotated to measure user believability in real versus fake news. Using this dataset, we address the problem of predicting news believability and study which user or news characteristics predict believability in real and fake news, as well as checking whether believability enhances fake news detection. We employ machine learning models incorporating news style, emotional content, and user traits to predict believability and apply explainability methods to clarify key characteristics influencing user belief. Overall, the models achieved significant results in detecting news believability and several news and user-based features such as writing style, emotion, personality, and psychology have been individuated as strong predictors of believability in news. We further integrate believability insights into advanced fake news detectors, demonstrating improved performance. To our knowledge, this is the first large-scale human-annotated English dataset designed for studying news believability, which we have released for use in future research.

The surge of research on fake news and misinformation in the aftermath of the 2016 election has led to a significant increase in publicly available source code repositories. Our study aims to systematically analyze and evaluate the most relevant repositories and their Python source code in this area to improve awareness, quality, and understanding of these resources within the research community. Additionally, our work aims to measure the quality and complexity metrics of these repositories and identify their fundamental features to aid researchers in advancing the field’s knowledge in understanding and preventing the spread of misinformation on social media. As a result, we found that more popular fake news repositories and associated papers with higher citation counts tend to have more maintainable code measures, more complex code paths, a larger number of lines of code, a higher Halstead effort, and fewer comments. Utilizing these findings to devise efficient research and coding techniques to combat fake news, we can strive towards building a more knowledgeable and well-informed society.

This paper addresses the problem of identifying sockpuppet accounts on Wikipedia. We formulate the problem as a binary classification task and propose a set of features based on user activity and the semantics of their contributions to separate sockpuppets from benign users. We tested our system on a dataset we built (and released to the research community) containing 17K accounts validated as sockpuppets. Experimental results show that our approach achieves an F1-score of 0.82 and outperforms other systems proposed in the literature. Moreover, our proposed approach is able to achieve an F1-score of 0.73 at detecting sockpuppet accounts by just considering their first edit.

In this paper, we take a step towards understanding how to design search engine results pages (SERP) that encourage children’s engagement as they seek for online resources. For this, we conducted a participatory design session to enable us to elicit children’s preferences and determine what children (ages 6–12) find lacking in more traditional SERP. We learned that children want more dynamic means of navigating results and additional ways to interact with results via icons. We use these findings to inform the design of a new SERP interface, which we denoted CHIRP. To gauge the type of engagement that a SERP incorporating interactive elements–CHIRP–can foster among children, we conducted a user study at a public school. Analysis of children’s interactions with CHIRP, in addition to responses to a post-task survey, reveals that adding additional interaction points results in a SERP interface that children prefer, but one that does not necessarily change engagement levels through clicks or time spent on SERP.