Detecting critical responses from deliberate self-harm videos on YouTube

YouTube is one of the leading social media platforms and online spaces for people who self-harm to search and view deliberate self-harm videos, share their experience and seek help via comments. These comments may contain information that signals a commentator could be at risk of potential harm. Due to a large amount of responses generated from these videos, it is very challenging for social media teams to respond to a vulnerable commentator who is at risk. We considered this issue as a multi-class problem and triaged viewers' comments into one of four severity levels. Using current state-of-the-art classifiers, we propose a model enriched with psycho-linguistic and sentiment features that can detect critical comments in need of urgent support. On average, our model achieved up to 60% precision, recall, and f1-score which indicates the effectiveness of the model

Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions

In online communities, antisocial behavior such as trolling disrupts constructive discussion. While prior work suggests that trolling behavior is confined to a vocal and antisocial minority, we demonstrate that ordinary people can engage in such behavior as well. We propose two primary trigger mechanisms: the individual's mood, and the surrounding context of a discussion (e.g., exposure to prior trolling behavior). Through an experiment simulating an online discussion, we find that both negative mood and seeing troll posts by others significantly increases the probability of a user trolling, and together double this probability. To support and extend these results, we study how these same mechanisms play out in the wild via a data-driven, longitudinal analysis of a large online news discussion community. This analysis reveals temporal mood effects, and explores long range patterns of repeated exposure to trolling. A predictive model of trolling behavior shows that mood and discussion context together can explain trolling behavior better than an individual's history of trolling. These results combine to suggest that ordinary people can, under the right circumstances, behave like trolls.Comment: Best Paper Award at CSCW 201

Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies

Intelligent conversational agents, or chatbots, can take on various identities and are increasingly engaging in more human-centered conversations with persuasive goals. However, little is known about how identities and inquiry strategies influence the conversation's effectiveness. We conducted an online study involving 790 participants to be persuaded by a chatbot for charity donation. We designed a two by four factorial experiment (two chatbot identities and four inquiry strategies) where participants were randomly assigned to different conditions. Findings showed that the perceived identity of the chatbot had significant effects on the persuasion outcome (i.e., donation) and interpersonal perceptions (i.e., competence, confidence, warmth, and sincerity). Further, we identified interaction effects among perceived identities and inquiry strategies. We discuss the findings for theoretical and practical implications for developing ethical and effective persuasive chatbots. Our published data, codes, and analyses serve as the first step towards building competent ethical persuasive chatbots.Comment: 15 pages, 10 figures. Full paper to appear at ACM CHI 202

A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts

Wide usage of social media platforms has increased the risk of aggression, which results in mental stress and affects the lives of people negatively like psychological agony, fighting behavior, and disrespect to others. Majority of such conversations contains code-mixed languages[28]. Additionally, the way used to express thought or communication style also changes from one social media plat-form to another platform (e.g., communication styles are different in twitter and Facebook). These all have increased the complexity of the problem. To solve these problems, we have introduced a unified and robust multi-modal deep learning architecture which works for English code-mixed dataset and uni-lingual English dataset both.The devised system, uses psycho-linguistic features and very ba-sic linguistic features. Our multi-modal deep learning architecture contains, Deep Pyramid CNN, Pooled BiLSTM, and Disconnected RNN(with Glove and FastText embedding, both). Finally, the system takes the decision based on model averaging. We evaluated our system on English Code-Mixed TRAC 2018 dataset and uni-lingual English dataset obtained from Kaggle. Experimental results show that our proposed system outperforms all the previous approaches on English code-mixed dataset and uni-lingual English dataset.Comment: 10 pages, 5 Figures, 6 Tables, accepted at CoDS-COMAD 202

Empathy, engagement, entrainment: the interaction dynamics of aesthetic experience

A recent version of the view that aesthetic experience is based in empathy as inner imitation explains aesthetic experience as the automatic simulation of actions, emotions, and bodily sensations depicted in an artwork by motor neurons in the brain. Criticizing the simulation theory for committing to an erroneous concept of empathy and failing to distinguish regular from aesthetic experiences of art, I advance an alternative, dynamic approach and claim that aesthetic experience is enacted and skillful, based in the recognition of others’ experiences as distinct from one’s own. In combining insights from mainly psychology, phenomenology, and cognitive science, the dynamic approach aims to explain the emergence of aesthetic experience in terms of the reciprocal interaction between viewer and artwork. I argue that aesthetic experience emerges by participatory sense-making and revolves around movement as a means for creating meaning. While entrainment merely plays a preparatory part in this, aesthetic engagement constitutes the phenomenological side of coupling to an artwork and provides the context for exploration, and eventually for moving, seeing, and feeling with art. I submit that aesthetic experience emerges from bodily and emotional engagement with works of art via the complementary processes of the perception–action and motion–emotion loops. The former involves the embodied visual exploration of an artwork in physical space, and progressively structures and organizes visual experience by way of perceptual feedback from body movements made in response to the artwork. The latter concerns the movement qualities and shapes of implicit and explicit bodily responses to an artwork that cue emotion and thereby modulate over-all affect and attitude. The two processes cause the viewer to bodily and emotionally move with and be moved by individual works of art, and consequently to recognize another psychological orientation than her own, which explains how art can cause feelings of insight or awe and disclose aspects of life that are unfamiliar or novel to the viewer

Computing for social science: Characterizing, quantifying, and analyzing social phenomena in technology mediated communications

Traditional social science methods of analyzing unstructured and semi-structured qualitative content often rely on labor and time intensive methods to transform qualitative data into quantitative representations of phenomena of interest. In order to rapidly conduct such social scientific research on large-scale data, social science researchers need to incorporate computational tools and methods. The Computational Social Science (CSS) paradigm offers useful perspectives for gaining insights from large-scale analyses of demographic, behavioral, social network, and technology-mediated communication data to investigate human activity, relationships, and other phenomena at multiple scales (e.g., individual, organizational, community, social group, and societal). Human-Centered Computing (HCC) complements CSS in this context by offering foundational science for designing, developing, evaluating, and deploying computational artifacts that better support the human endeavors associated with the conduct and practice of CSS research. This dissertation demonstrates theoretical, methodological, and technological contributions resulting from blending traditional social science with computational approaches for the study of human cognition and behavior. Following the CSS paradigm, I build theoretically-informed representations of social constructs—e.g., models of interpersonal relationships and the complex cognitive processes related to human perceptions of sentiment and bias—using HCC methods and principles to develop and evaluate computational tools that implement those models for the purpose of aiding social science research oriented around large-scale text-based analysis of content from social media networks, product and movie reviews, and newspapers.Ph.D