Cliff Lampe’s research while affiliated with Concordia University Ann Arbor and other places

... Partially due to conceptual heterogeneity, there is no universally accepted definition of digital hate targets. 1 Many targets are defined based on their age, gender, sexual orientation, race, ethnicity, religion, physical appearance, disability, and socioeconomic status (Castaño-Pulgarín et al., 2021;Celuch et al., 2023). In general, children and adolescents (Zhu et al., 2021), women (Chen et al., 2020), the LGBTQIA+ community (Schoenebeck et al., 2023), people of color (POC; Keum, 2023), Muslims (Evolvi, 2019), and disabled people (Moral et al., 2022) are considered typical targets from socially non-empowered groups. In recent years, some polarized issues such as the COVID-19 pandemic have given rise to digital hate against societally empowered groups such as politicians, journalists, and academics whose work may include maintaining an online presence and the dissemination of information with broad and diverse online audiences (Farrell et al., 2020). ...

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Listen to Me! Target Perceptions of Digital Hate: A Scoping Review of Recent Research

... People may have unrealistic expectations of LLMs and use them inappropriately [76], such as by assuming LLMs are skilled at math simply because they are computational models [93]. People also demonstrate divergent decision-making characteristics, with some preferring to meticulously optimize their decision while others take the satisficing or minimizing paths that allow them to bypass extensive thinking [34,59]. Those who minimize time spent on tasks may be more likely to over-rely on LLM advice, given its perceived completeness and relevance [23]. ...

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To Rely or Not to Rely? Evaluating Interventions for Appropriate Reliance on Large Language Models

... (ii) forensics: multiple unrelated accounts used by the same subject can be linked by matching their typing behavior, allowing the identification or shortlisting of malicious users. Such measures could help in contrasting toxicity, hate, and harassment on social networks [5], protecting minors from online grooming [6], preventing the spread of fake news [7] as well as "Wikipedia wars" [8]. (iii) privacy protection: in biometrics, specific patterns in the input data are often associated with demographic groups (such as gender, age, ethnicity). ...

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KVC-onGoing: Keystroke Verification Challenge

... While positive popularity metrics signal social endorsement of information (e.g., upvotes, likes), negative indicators of popularity (e.g., downvotes, dislikes) signal disapproval (Lee et al., 2022). However, negative indicators are becoming obsolete and are no longer visible on standard platforms such as https://Reddit.com ...

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Moderators of Curiosity and Information Seeking in Younger and Older Adults

... Studies indicate that the context in which violence is depicted influences whether viewers perceive it as acceptable. For instance, Blackwell et al. (2018) concluded that adolescents are more likely to justify media violence if it aligns with their personal experiences or societal norms that they have internalized. This is particularly true when media characters use violence for self-defense or when they are portrayed as heroes. ...

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Relationship between Exposure to Violent Content on the Media and Aggressive Behaviour among Adolescent Learners in Western Region, Kenya

... XAI aims to bridge the gap between complex machine learning models and end-users, particularly in domains where decisions have compliance, ethical, legal, or social implications Kaur et al., 2022;Tull et al., 2024). However, the current landscape of XAI evaluation is fragmented, with metrics used to assess model interpretability and explanation quality remaining inconsistent and subjective (Madsen et al., 2024). ...

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Bridging the Gap in XAI-Why Reliable Metrics Matter for Explainability and Compliance

... As a result, they may also potentially form networks to support each other during hardships. Similar patterns have been reported in other contexts as the collective action of loosely knit online networks of people who never met each other (Poor et al., 2022). In 2020, after laying the foundation for constructing the first Hindu worship place in the capital of Pakistan, extremists spread hate speech against Hindus and vandalized the construction site. ...

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From Silicon to Himalayan Valley(s): Tales of Serendipitous Exposure and Interfaith Contact on Social Media

... Work on online trolls (Buckels et al. 2014) found that, compared to other groups, people who liked trolling scored significantly higher on the dark tetrad of personality traits: psychopathy, sadism, Machiavellianism, and narcissism. More recent work that looked at online harassers (Lee et al. 2022) found that impulsivity, reactive aggression, and premeditated aggression were distinguishing characteristics of harassers. ...

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Misogynistic targeting of women in power predicts broader online harassment patterns

... Amazon Mechanical Turk (MTurk) has been frequently used to recruit participants for online scenario-based studies related to AI technologies (Antes et al. 2021;Kim, Giroux, and Lee 2021;Kaur, Lampe, and Lasecki 2020). We recruited 200 participants from the United States through Amazon Mechanical Turk. ...

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Trusting Your AI Agent Emotionally and Cognitively: Development and Validation of a Semantic Differential Scale for AI Trust

... In this theory, anonymity could lead to this loss of control. This loss of control could then lead to deviant behavior, which may violate the community norms that are likely to be targets of moderation on Reddit, and are likely to be removed by moderation [32]. While anonymous editors on Wikipedia are more prone to violating policies related to edit warring, particularly on discussion pages [33], Reddit users engage with the platform for a wider range of activities beyond information sharing and editing [34], [35]. ...

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Throwaway Accounts and Moderation on Reddit