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1. Living With Everyday Evaluations on Social Media Platforms

   Hallinan, Blake; Brubaker, Jed R. (January 2021, International journal of communication)

   null (Ed.)

   The Like button is simultaneously a means of social interaction and a tool to evaluate social media content. Likes are built into the infrastructure of platforms, fueling personalized searches, recommendations, and targeted advertisements while contributing to the economic valuation of platforms and users alike. Working at the intersection of infrastructure and practice, we analyze the experience of everyday evaluations on social media to understand how platforms shape what people care about. Based on in-depth interviews with 25 artists who use Instagram, we identify three overlapping orientations to the Like button: affective, relational, and infrastructural. We show how the flexibility of the button creates ambiguity around the meaning of a Like which drives practices of metaevaluation, and argue that the platform architecture incentivizes an economic approach to evaluation that crowds out other value schemas, shaping how artists use the platform, make art, and even understand themselves. We conclude with a discussion of the broader implications of everyday evaluations, algorithms, and platforms. 

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2. When research is the context: Cross-platform user expectations for social media data reuse

   https://doi.org/10.1177/20539517231164108  

   Gilbert, Sarah; Shilton, Katie; Vitak, Jessica (January 2023, Big Data & Society)

   Social media provides unique opportunities for researchers to learn about a variety of phenomena—it is often publicly available, highly accessible, and affords more naturalistic observation. However, as research using social media data has increased, so too has public scrutiny, highlighting the need to develop ethical approaches to social media data use. Prior work in this area has explored users’ perceptions of researchers’ use of social media data in the context of a single platform. In this paper, we expand on that work, exploring how platforms and their affordances impact how users feel about social media data reuse. We present results from three factorial vignette surveys, each focusing on a different platform—dating apps, Instagram, and Reddit—to assess users’ comfort with research data use scenarios across a variety of contexts. Although our results highlight different expectations between platforms depending on the research domain, purpose of research, and content collected, we find that the factor with the greatest impact across all platforms is consent—a finding which presents challenges for big data researchers. We conclude by offering a sociotechnical approach to ethical decision-making. This approach provides recommendations on how researchers can interpret and respond to platform norms and affordances to predict potential data use sensitivities. The approach also recommends that researchers respond to the predominant expectation of notification and consent for research participation by bolstering awareness of data collection on digital platforms. 

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3. Cross-platform Information Operations: Mobilizing Narratives & Building Resilience through both 'Big' & 'Alt' Tech

   https://doi.org/10.1145/3476086  

   Wilson, Tom; Starbird, Kate (October 2021, Proceedings of the ACM on Human-Computer Interaction)

   Despite increasing awareness and research about online strategic information operations, there remain gaps in our understanding, including how information operations leverage the wider information ecosystem and take shape on and across multiple social media platforms. In this paper we use mixed methods, including digital trace ethnography, to look beyond a single social media platform to the broader information ecosystem. We aim to understand how multiple social media platforms are used, in parallel and complementary ways, to achieve the strategic goals of online information operations. We focus on a specific case study: the contested online conversation surrounding Syria Civil Defense (the White Helmets), a group of first responders that assists civilians affected by the civil war within the country. Our findings reveal a network of social media platforms from which content is produced, stored, and integrated into the Twitter conversation. We highlight specific activities that sustain the strategic narratives and attempt to influence the media agenda. And we note that underpinning these efforts is the work of resilience-building: the use of alternative (non-mainstream) platforms to counter perceived threats of 'censorship' by large, established social media platforms. We end by discussing the implications on social media platform policy. 

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4. Cognitive Implications of Engaging With “Fitspiration” Content on Social Media

   https://doi.org/10.1525/collabra.37458  

   Zimmerman, Jordan; De Rezende, Angelica; Wright, Anna M.; Lord, Kaitlin M.; Brown-Schmidt, Sarah (January 2022, Collabra: Psychology)

   Social media is a routine part of every-day life for millions of people worldwide. How does engaging with social media shape enduring memories for that experience? This question is important given the popularity of certain types of content on social media platforms, such as content widely known as “fitspiration”. Two experiments involving 510 US adults (mean age = 36.82) examined memory for food and fitness-related social media images that individuals write comments about, as well as memory for other images in the context. We demonstrate that commenting on social media images boosts memory for them and weakly affects memory for conceptually related images in the same context. Exploratory analyses revealed correlations between self-reported disordered eating symptomology and effects of commenting on memory. These findings demonstrate that how people engage with social media has implications for the enduring memories of that content and may relate to behaviors and attitudes in offline lives, such as eating and body image. 

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5. A Heterogeneous Multimodal Graph Learning Framework for Recognizing User Emotions in Social Networks

   Bhattacharyya, Sree; Yang, Shuhua; Wang, James Z (September 2024, Proceedings of the International Conference on Affective Computing and Intelligent Interaction)

   The rapid expansion of social media platforms has provided unprecedented access to massive amounts of multimodal user-generated content. Comprehending user emotions can provide valuable insights for improving communication and understanding of human behaviors. Despite significant advancements in Affective Computing, the diverse factors influencing user emotions in social networks remain relatively understudied. Moreover, there is a notable lack of deep learning-based methods for predicting user emotions in social networks, which could be addressed by leveraging the extensive multimodal data available. This work presents a novel formulation of personalized emotion prediction in social networks based on heterogeneous graph learning. Building upon this formulation, we design HMG-Emo, a Heterogeneous Multimodal Graph Learning Framework that utilizes deep learning-based features for user emotion recognition. Additionally, we include a dynamic context fusion module in HMG-Emo that is capable of adaptively integrating the different modalities in social media data. Through extensive experiments, we demonstrate the effectiveness of HMG-Emo and verify the superiority of adopting a graph neural network-based approach, which outperforms existing baselines that use rich hand-crafted features. To the best of our knowledge, HMG-Emo is the first multimodal and deep-learning-based approach to predict personalized emotions within online social networks. Our work highlights the significance of exploiting advanced deep learning techniques for less-explored problems in Affective Computing. 

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