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1. "Did you miss my comment or what?": understanding toxicity in open source discussions

   https://doi.org/10.1145/3510003.3510111  

   Miller, Courtney; Cohen, Sophie; Klug, Daniel; Vasilescu, Bogdan; KaUstner, Christian (May 2022, International Conference on Software Engineering)

   Online toxicity is ubiquitous across the internet and its negative impact on the people and that online communities that it effects has been well documented. However, toxicity manifests differently on various platforms and toxicity in open source communities, while frequently discussed, is not well understood. We take a first stride at understanding the characteristics of open source toxicity to better inform future work on designing effective intervention and detection methods. To this end, we curate a sample of 100 toxic GitHub issue discussions combining multiple search and sampling strategies. We then qualitatively analyze the sample to gain an understanding of the characteristics of open-source toxicity. We find that the pervasive forms of toxicity in open source differ from those observed on other platforms like Reddit or Wikipedia. In our sample, some of the most prevalent forms of toxicity are entitled, demanding, and arrogant comments from project users as well as insults arising from technical disagreements. In addition, not all toxicity was written by people external to the projects; project members were also common authors of toxicity. We also discuss the implications of our findings. Among others we hope that our findings will be useful for future detection work. 

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2. Quantifying the Intensity of Toxicity for Discussions and Speakers

   https://doi.org/10.1109/ACIIW52867.2021.9666258  

   Samrose, Samiha; Hoque, Ehsan (September 2021, 2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW))

   In this work, from YouTube News-show multimodal dataset with dyadic speakers having heated discussions, we analyze the toxicity through audio-visual signals. Firstly, as different speakers may contribute differently towards the toxicity, we propose a speaker-wise toxicity score revealing individual proportionate contribution. As discussions with disagreements may reflect some signals of toxicity, in order to identify discussions needing more attention we categorize discussions into binary high-low toxicity levels. By analyzing visual features, we show that the levels correlate with facial expressions as Upper Lid Raiser (associated with ‘surprise’), Dimpler (associated with ‘contempť), and Lip Corner Depressor (associated with ‘disgust’) remain statistically significant in separating high-low intensities of disrespect. Secondly, we investigate the impact of audio-based features such as pitch and intensity that can significantly elicit disrespect, and utilize the signals in classifying disrespect and non-disrespect samples by applying logistic regression model achieving 79.86% accuracy. Our findings shed light on the potential of utilizing audio-visual signals in adding important context towards understanding toxic discussions. 

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3. Privacy, Anonymity, and Perceived Risk in Open Collaboration: A Study of Service Providers

   https://doi.org/10.1145/3290605.3300901  

   McDonald, Nora; Hill, Benjamin Mako; Greenstadt, Rachel; Forte, Andrea (May 2019, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19))

   Anonymity can enable both healthy online interactions like support-seeking and toxic behaviors like hate speech. How do online service providers balance these threats and opportunities? This two-part qualitative study examines the challenges perceived by open collaboration service providers in allowing anonymous contributions to their projects. We interviewed eleven people familiar with organizational decisions related to privacy and security at five open collaboration projects and followed up with an analysis of public discussions about anonymous contribution to Wikipedia. We contrast our findings with prior work on threats perceived by project volunteers and explore misalignment between policies aiming to serve contributors and the privacy practices of contributors themselves. 

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4. Short-Term Chronic Toxicity of Copper to Hyalella azteca: Contrast in Terms of Equilibrating Diet, Diet Type, and Organic Matter Source

   https://doi.org/10.3390/toxics12080608  

   Fuad, Nafis; Williams, Rebecca; Vadas, Timothy M (August 2024, Toxics)

   The most up-to-date regulatory guidelines for establishing acute and chronic numeric limits for copper in freshwaters are based on a biotic ligand model for various species, but the model for Cu lacks data on dietary uptake. In addition, some common macroinvertebrate toxicity assay parameters are less representative of the ecosystem. We investigated the effects of diet and its type in the experimental setup and as an exposure pathway to an established amphipod (crustacean) Hyalella azteca (H. azteca) for Cu toxicity assays. We also investigated another overlooked aspect, the organic matter (OM) source. Our experiments compared the toxicity of pre-equilibrated and unequilibrated natural diets and a laboratory-favored diet in effluent and stormwater sources of organic matter adjusted to standard water characteristics. The experiments indicated a more toxic effect of the pre-equilibrated diet and natural dietary sources, and less toxic effects in the presence of effluent OM compared with stormwater OM, shifting LC50 or EC20 values by as much as 67% compared with the controls. The use of a pre-equilibrated natural diet in toxicity assays provides the advantage of producing toxicity data more representative of field conditions. Considering organic matter type, especially in dietary exposures, will better predict toxicity, accounting for copper complexation with OM from different sources and partitioning to the food supply. Adapting these ecologically relevant parameters in whole effluent toxicity testing or other assays will also provide safer regulatory oversite of discharges to surface waters. 

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5. Toxicity by Game Design: How Players Perceive the Influence of Game Design on Toxicity

   https://doi.org/10.1145/3677110  

   Zhang, Zinan; Moradzadeh, Sam; Woan, Andrew; Kou, Yubo (October 2024, Proceedings of the ACM on Human-Computer Interaction)

   Toxicity in online games refers to behaviors where players disrupt the gaming experience of others, leading to adverse outcomes such as depression and low self-esteem. Although scholars have identified various factors contributing to toxicity, ranging from individual motivations to team dynamics to cultural backgrounds, the role of game design has been less frequently discussed. To bridge this gap, we conducted an interview study to explore players' perceptions of how game design influences toxicity. Our research identified four game design elements that participants perceived as contributing factors to the emergency of toxicity in their experiences: team interdependency, fairness, interaction design, and privacy. These findings help us shed light on how game design unintendedly triggers toxic intentions, exposes players to vulnerability, making them potential victims, and affects player interactions which lead to toxicity. We further propose design implications that can mitigate toxicity in online games. 

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