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1. An Investigation on the Portrayal of Blue Whale Challenge on YouTube and Twitter

   https://doi.org/10.1177/1071181319631179  

   Khasawneh, Amro ; Chalil Madathil, Kapil ; Dixon, Emma ; Wisniewski, Pamela ; Zinzow, Heidi ; Roth, Rebecca ( November 2019 , Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Introduction Social media has created opportunities for children to gather social support online (Blackwell et al., 2016; Gonzales, 2017; Jackson, Bailey, & Foucault Welles, 2018; Khasawneh, Rogers, Bertrand, Madathil, & Gramopadhye, 2019; Ponathil, Agnisarman, Khasawneh, Narasimha, & Madathil, 2017). However, social media also has the potential to expose children and adolescents to undesirable behaviors. Research showed that social media can be used to harass, discriminate (Fritz & Gonzales, 2018), dox (Wood, Rose, & Thompson, 2018), and socially disenfranchise children (Page, Wisniewski, Knijnenburg, & Namara, 2018). Other research proposes that social media use might be correlated to the significant increase in suicide rates and depressive symptoms among children and adolescents in the past ten years (Mitchell, Wells, Priebe, & Ybarra, 2014). Evidence based research suggests that suicidal and unwanted behaviors can be promulgated through social contagion effects, which model, normalize, and reinforce self-harming behavior (Hilton, 2017). These harmful behaviors and social contagion effects may occur more frequently through repetitive exposure and modelling via social media, especially when such content goes “viral” (Hilton, 2017). One example of viral self-harming behavior that has generated significant media attention is the Blue Whale Challenge (BWC). The hearsay about this challenge is that individuals at all ages are persuaded to participate in self-harm and eventually kill themselves (Mukhra, Baryah, Krishan, & Kanchan, 2017). Research is needed specifically concerning BWC ethical concerns, the effects the game may have on teenagers, and potential governmental interventions. To address this gap in the literature, the current study uses qualitative and content analysis research techniques to illustrate the risk of self-harm and suicide contagion through the portrayal of BWC on YouTube and Twitter Posts. The purpose of this study is to analyze the portrayal of BWC on YouTube and Twitter in order to identify the themes that are presented on YouTube and Twitter posts that share and discuss BWC. In addition, we want to explore to what extent are YouTube videos compliant with safe and effective suicide messaging guidelines proposed by the Suicide Prevention Resource Center (SPRC). Method Two social media websites were used to gather the data: 60 videos and 1,112 comments from YouTube and 150 posts from Twitter. The common themes of the YouTube videos, comments on those videos, and the Twitter posts were identified using grounded, thematic content analysis on the collected data (Padgett, 2001). Three codebooks were built, one for each type of data. The data for each site were analyzed, and the common themes were identified. A deductive coding analysis was conducted on the YouTube videos based on the nine SPRC safe and effective messaging guidelines (Suicide Prevention Resource Center, 2006). The analysis explored the number of videos that violated these guidelines and which guidelines were violated the most. The inter-rater reliabilities between the coders ranged from 0.61 – 0.81 based on Cohen’s kappa. Then the coders conducted consensus coding. Results & Findings Three common themes were identified among all the posts in the three social media platforms included in this study. The first theme included posts where social media users were trying to raise awareness and warning parents about this dangerous phenomenon in order to reduce the risk of any potential participation in BWC. This was the most common theme in the videos and posts. Additionally, the posts claimed that there are more than 100 people who have played BWC worldwide and provided detailed description of what each individual did while playing the game. These videos also described the tasks and different names of the game. Only few videos provided recommendations to teenagers who might be playing or thinking of playing the game and fewer videos mentioned that the provided statistics were not confirmed by reliable sources. The second theme included posts of people that either criticized the teenagers who participated in BWC or made fun of them for a couple of reasons: they agreed with the purpose of BWC of “cleaning the society of people with mental issues,” or they misunderstood why teenagers participate in these kind of challenges, such as thinking they mainly participate due to peer pressure or to “show off”. The last theme we identified was that most of these users tend to speak in detail about someone who already participated in BWC. These videos and posts provided information about their demographics and interviews with their parents or acquaintances, who also provide more details about the participant’s personal life. The evaluation of the videos based on the SPRC safe messaging guidelines showed that 37% of the YouTube videos met fewer than 3 of the 9 safe messaging guidelines. Around 50% of them met only 4 to 6 of the guidelines, while the remaining 13% met 7 or more of the guidelines. Discussion This study is the first to systematically investigate the quality, portrayal, and reach of BWC on social media. Based on our findings from the emerging themes and the evaluation of the SPRC safe messaging guidelines we suggest that these videos could contribute to the spread of these deadly challenges (or suicide in general since the game might be a hoax) instead of raising awareness. Our suggestion is parallel with similar studies conducted on the portrait of suicide in traditional media (Fekete & Macsai, 1990; Fekete & Schmidtke, 1995). Most posts on social media romanticized people who have died by following this challenge, and younger vulnerable teens may see the victims as role models, leading them to end their lives in the same way (Fekete & Schmidtke, 1995). The videos presented statistics about the number of suicides believed to be related to this challenge in a way that made suicide seem common (Cialdini, 2003). In addition, the videos presented extensive personal information about the people who have died by suicide while playing the BWC. These videos also provided detailed descriptions of the final task, including pictures of self-harm, material that may encourage vulnerable teens to consider ending their lives and provide them with methods on how to do so (Fekete & Macsai, 1990). On the other hand, these videos both failed to emphasize prevention by highlighting effective treatments for mental health problems and failed to encourage teenagers with mental health problems to seek help and providing information on where to find it. YouTube and Twitter are capable of influencing a large number of teenagers (Khasawneh, Ponathil, Firat Ozkan, & Chalil Madathil, 2018; Pater & Mynatt, 2017). We suggest that it is urgent to monitor social media posts related to BWC and similar self-harm challenges (e.g., the Momo Challenge). Additionally, the SPRC should properly educate social media users, particularly those with more influence (e.g., celebrities) on elements that boost negative contagion effects. While the veracity of these challenges is doubted by some, posting about the challenges in unsafe manners can contribute to contagion regardless of the challlenges’ true nature. 

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2. Toward A Two-Sided Fairness Framework in Search and Recommendation

   https://doi.org/10.1145/3576840.3578332  

   Liu, Jiqun ( March 2023 , CHIIR '23: Proceedings of the 2023 Conference on Human Information Interaction and Retrieval)

   As artificial intelligence (AI) assisted search and recommender systems have become ubiquitous in workplaces and everyday lives, understanding and accounting for fairness has gained increasing attention in the design and evaluation of such systems. While there is a growing body of computing research on measuring system fairness and biases associated with data and algorithms, the impact of human biases that go beyond traditional machine learning (ML) pipelines still remain understudied. In this Perspective Paper, we seek to develop a two-sided fairness framework that not only characterizes data and algorithmic biases, but also highlights the cognitive and perceptual biases that may exacerbate system biases and lead to unfair decisions. Within the framework, we also analyze the interactions between human and system biases in search and recommendation episodes. Built upon the two-sided framework, our research synthesizes intervention and intelligent nudging strategies applied in cognitive and algorithmic debiasing, and also proposes novel goals and measures for evaluating the performance of systems in addressing and proactively mitigating the risks associated with biases in data, algorithms, and bounded rationality. This paper uniquely integrates the insights regarding human biases and system biases into a cohesive framework and extends the concept of fairness from human-centered perspective. The extended fairness framework better reflects the challenges and opportunities in users’ interactions with search and recommender systems of varying modalities. Adopting the two-sided approach in information system design has the potential to enhancing both the effectiveness in online debiasing and the usefulness to boundedly rational users engaging in information-intensive decision-making. 

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3. LuckyFind: Leveraging Surprise to Improve User Satisfaction and Inspire Curiosity in a Recommender System

   https://doi.org/10.1145/3406522.3446017  

   Niu, Xi ; Al-Doulat, Ahmad ( March 2021 , CHIIR '21: Proceedings of the 2021 ACM SIGIR Conference on Human Information Interaction and Retrieval)

   null (Ed.)

   The growing amount of online information today has increased opportunity to discover interesting and useful information. Various recommender systems have been designed to help people discover such information. No matter how accurately the recommender algorithms perform, users’ engagement with recommended results has been complained being less than ideal. In this study, we touched on two human-centered objectives for recommender systems: user satisfaction and curiosity, both of which are believed to play roles in maintaining user engagement and sustain such engagement in the long run. Specifically, we leveraged the concept of surprise and used an existing computational model of surprise to identify relevantly surprising health articles aiming at improving user satisfaction and inspiring their curiosity. We designed a user study to first test the validity of the surprise model in a health news recommender system, called LuckyFind. Then user satisfaction and curiosity were evaluated. We find that the computational surprise model helped identify surprising recommendations at little cost of user satisfaction. Users gave higher ratings on interestingness than usefulness for those surprising recommendations. Curiosity was inspired more for those individuals who have a larger capacity to experience curiosity. Over half of the users have changed their preferences after using LuckyFind, either discovering new areas, reinforcing their existing interests, or stopping following those they did not want anymore. The insights of the research will make researchers and practitioners rethink the objectives of today’s recommender systems as being more human-centered beyond algorithmic accuracy. 

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4. Testing a Recommender System for Self-Actualization

   Wilkinson, Daricia ; Sivakumar, Saadhika ; Sinha, Pratitee ; Knijnenburg, Bart P. ( January 2017 , Proceedings of the Second Workshop on Engineering Computer-Human Interaction in Recommender Systems co-located with the 9th ACM SIGCHI Symposium on Engineering Interactive Computing Systems)

   Traditionally, recommender systems were built with the goal of aiding users’ decision-making process by extrapolating what they like and what they have done to predict what they want next. However, in attempting to personalize the suggestions to users’ preferences, these systems create an isolated universe of information for each user, which may limit their perspectives and promote complacency. In this paper, we describe our research plan to test a novel approach to recommender systems that goes beyond “good recommendations” that supports user aspirations and exploration. 

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5. Optimizing Semantic Enrichment of Biomedical Content through Knowledge Sharing

   Abbas, Asim Abbas ; Mbouadeu, Steve ; Bisram, Avinash ; Iqbal, Nadeem ; Bukhari, Syed Ahmad ( September 2022 , Knowledge Graphs and Semantic Web . KGSWC 2022. Communications in Computer and Information Science, vol 1686. Springer, Cham.)

   Villazón-Terrazas, B. (Ed.)

   Each day a vast amount of unstructured content is generated in the biomedical domain from various sources such as clinical notes, research articles and medical reports. Such content contain a sufficient amount of efficient and meaningful information that needs to be converted into actionable knowledge for secondary use. However, accessing precise biomedical content is quite challenging because of content heterogeneity, missing and imprecise metadata and unavailability of associated semantic tags required for search engine optimization. We have introduced a socio-technical semantic annotation optimization approach that enhance the semantic search of biomedical contents. The proposed approach consist of layered architecture. At First layer (Preliminary Semantic Enrichment), it annotates the biomedical contents with the ontological concepts from NCBO BioPortal. With the growing biomedical information, the suggested semantic annotations from NCBO Bioportal are not always correct. Therefore, in the second layer (Optimizing the Enriched Semantic Information), we introduce a knowledge sharing scheme through which authors/users could request for recommendations from other users to optimize the semantic enrichment process. To guage the credibility of the the human recommended, our systems records the recommender confidence score, collects community voting against previous recommendations, stores percentage of correctly suggested annotation and translates that into an index to later connect right users to get suggestions to optimize the semantic enrichment of biomedical contents. At the preliminary layer of annotation from NCBO, we analyzed the n-gram strategy for biomedical word boundary identification. We have found that NCBO recognizes biomedical terms for n-gram-1 more than for n-gram-2 to n-gram-5. Similarly, a statistical measure conducted on significant features using the Wilson score and data normalization. In contrast, the proposed methodology achieves an suitable accuracy of ≈90% for the semantic optimization approach. 

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