Atmospheric image of the exhibited self: Gâvur Mahallesi exhibition on Instagram

Using Digital Media to Promote Kidney Disease Education

This article assesses the usefulness for social media research of controversy analysis, an approach developed in Science and Technology Studies (STS) and related fields. We propose that this approach can help to address an important methodological problem in social media research, namely, the tension between social media as resource for social research and as an empirical object in its own right. Initially developed for analyzing interactions between science, technology, and society, controversy analysis has in recent decades been implemented digitally to study public debates and issues dynamics online. A key feature of controversy analysis as a digital method, we argue, is that it enables a symmetrical approach to the study of media-technological dynamics and issue dynamics. It allows us to pay equal attention to the ways in which a digital platform like Twitter mediates public issues, and to how controversies mediate “social media” as an object of public attention. To sketch the contours of such a symmetrical approach, the article discusses examples from a recent social media research project in which we mapped issues of “privacy” and “surveillance” in the wake of the National Security Agency (NSA) data leak by Edward Snowden in June 2013. Through a discussion of social media research practice, we then outline a symmetrical approach to analyzing controversy with social media. We conclude that the digital implementation of such an approach requires further exchanges between social media researchers and controversy analysts.

Solace in Social Media: Women Unite Under COVID-19

This paper discusses the implementation of methods of controversy analysis in social media research, and proposes that if project is to be successful, we need to address the tension between social media as a resource for social inquiry and as an empirical object in its own right (Thielmann, 2012; see also Hilgartner, 2000). Controversy analysis is an approach developed in Social Studies of Science and Technology (STS) and related fields for examining disputes about science, technology and society (Latour, 1987; Nelkin 1971), and it has also been implemented digitally in order to study public debates and issues dynamics (Rogers & Marres, 2000, Venturini 2010). A key feature of controversy analysis as a digital social method, we argue, is that it enables a symmetrical approach to the study of media-technological dynamics and substantive dynamics of issue formastion. It allows us to pay attention to the ways in which a digital platform like Twitter mediates public issues, but also to how controversies mediate “social media”, as an object of public attention and infrastructure that participates in the organisation of issue engagement. To showcase this symmetrical approach, the paper will present a pilot study in Twitter analysis, in which we mapped issues of ‘privacy’ and ‘surveillance’ after the Snowden affair broke in the summer of 2013. We argue in favor of maintaining the instability of medium and controversy, but suggest that the digital implementation of controversy analysis requires further elaboration and productive dialogue between researchers of social media practices and researchers of public controversy.

Purpose/Rationale The study examines how consumers look at sponsorship messaging embedded within Instagram images on the International Volleyball Federation’s official beach volleyball account, investigating how social media consumers dedicate visual attention to the photographs. Design The study followed an experimental design using eye-tracking technology with a within-subjects protocol. Researchers recorded the gaze behavior of participants (n = 41) on social media images (n = 18) featuring beach volleyball athletes who played on the 2023 Volleyball World Beach Pro Tour Elite16. Findings The study’s results revealed that consumers dedicated most of their visual attention to the athletes in the images compared to the sponsorship messaging. In addition, participants were less likely to look at sponsors when the athletes were in defensive positions compared to offensive positions or posed positions. Practical implications The lack of visual attention to sponsorship messaging reinforces the need for social media marketers to be strategic in how they select images for Instagram and incorporate sponsors into their social media feeds. Research contribution Results highlight the need to better understand how consumers dedicate visual attention to sponsors embedded within social media posts. Originality/Value This article widens the methodological diversity in sports and social media research by using eye-tracking technology.

With the expansion and popularity of research on websites such as Facebook and Twitter, there has been increasing concern about investigator conduct and social media ethics. The availability of large data sets has attracted researchers who are not traditionally associated with health data and its associated ethical considerations, such as computer and data scientists. Reliance on oversight by ethics review boards is inadequate and, due to the public availability of social media data, there is often confusion between public and private spaces. In addition, social media participants and researchers may pay little attention to traditional terms of use. In this paper, we review four cases involving ethical and terms-of-use violations by researchers seeking to conduct social media studies in an online patient research network. These violations involved unauthorized scraping of social media data, entry of false information, misrepresentation of researcher identities of participants on forums, lack of ethical approval and informed consent, use of member quotations, and presentation of findings at conferences and in journals without verifying accurate potential biases and limitations of the data. The correction of these ethical lapses often involves much effort in detecting and responding to violators, addressing these lapses with members of an online community, and correcting inaccuracies in the literature (including retraction of publications and conference presentations). Despite these corrective actions, we do not regard these episodes solely as violations. Instead, they represent broader ethical issues that may arise from potential sources of confusion, misinformation, inadequacies in applying traditional informed consent procedures to social media research, and differences in ethics training and scientific methodology across research disciplines. Social media research stakeholders need to assure participants that their studies will not compromise anonymity or lead to harmful outcomes while preserving the societal value of their health-related studies. Based on our experience and published recommendations by social media researchers, we offer potential directions for future prevention-oriented measures that can be applied by data producers, computer/data scientists, institutional review boards, research ethics committees, and publishers.

Recent years have seen an explosion in social media in our everyday lives, and a corresponding increase in social media research in IS. As social media researchers, we are intrigued by the problem of virtuality and context in social media research, and how we might apply reflexive research principles to such settings. In social media, the absence of a setting's real physical boundaries (to a large extent) limits participants' ability to create a common experience at the present time and develop a history of shared experiences. As a result, we would contend that many social media researchers' interpretations of data in social media settings are often black‐boxed. In this paper, we argue that many of the challenges concerned with social media settings, by nature, are emergent and linked to their virtual and contextual features. We use the Klein and Myers (1999) framework for traditional interpretive field studies as a vehicle for unpacking these challenges. We contend that these challenges may remain unnoticed if researchers do not actively reflect upon their impact on the research process. In this paper, we present a framework for social media research, considering social media research as a reflexive space, building on the notion of three levels of reflexivity: theory, design and practice. Finally, we discuss some implications of reflexivity for qualitative social media research in IS.

Introduction I am on the phone with a journalist discussing my research into anti-vaccination. As the conversation winds up, they ask a question I have come to expect: "how big do you think this is?" My answer is usually some version of the following: that we have no way of knowing. I and my fellow researchers can only see the information that is public or in the sunlight. How anti-vaccination information spreads through private networks is dark to us. It is private and necessarily so. This means that we cannot track how these conversations spread in the private or parochial spaces of Facebook, nor can we consider how they might extend into other modes of mediated communication. Modern communication is a complex and multiplatform accomplishment. Consider this: I am texting with my friend, I send her a selfie, in the same moment I hear a notification, she has DMed me a relevant Instagram post via that app. I move to Instagram and share another post in response; we continue our text message conversation there. Later in the day, I message her on Facebook Messenger while participating in a mutual WhatsApp group chat. The next day we Skype, and while we talk, we send links back and forth, which in hindsight are as clear as hieroglyphics before the Rosetta stone. I comment on her Twitter post, and we publicly converse back and forth briefly while other people like our posts. None of these instances are discrete conversational events, even though they occur on different platforms. They are iterations on the same themes, and the archival properties of social media and private messaging apps mean that neither of you forgets where you left off. The conversation slides not only between platforms and contexts but in and out of visibility. Digitally mediated conversation hums in the background of daily life (boring meetings, long commutes and bad dates) and expands our understanding of the temporal and sequential limits of conversation. In this article, I will explore digitally-mediated cross-platform conversation as a problem in two parts, and how we can understand it as part of the 'dark social'. Specifically, I want to draw attention to how 'dark' online spaces are part of our everyday communicative practices and are not necessarily synonymous with the illicit, illegal, or deviant. I argue that the private conversations we have online are also part of the dark social web, insofar as they are hidden from the public eye. When I think of dark social spaces, I think of what lies beneath the surface of murky waters, what hides behind in backstage areas, and the moments between platforms. In contrast, 'light' (or public) social spaces are often perceived as siloed. The boundaries between these platforms are artificially clean and do not appear to leak into other spaces. This article explores the dark and shadowed spaces of online conversation and considers how we might approach them as researchers. Conversations occur in the backchannels of social media platforms, in private messaging functions that are necessarily invisible to the researcher's gaze. These spaces are distinct from the social media activity analysed by Marwick and boyd. Their research examining teens' privacy strategies on social media highlights how social media posts that multiple audiences may view often hold encoded meanings. Social media posts are a distinct and separate category of activity from meditated conversations that occur one to one, or in smaller group chat settings. Second is the disjunction between social media platforms. Users spread their activity across any number of social media platforms, according to social and personal logics. However, these movements are difficult to capture; it is difficult to see in the dark. Platforms are not hermeneutically sealed off from each other, or the broader web. I argue that understanding how conversation moves between platforms and in the backstage spaces of platforms are two parts of the same dark social puzzle. Conversation Online Digital media have changed how we maintain our social connections across time and space. Social media environments offer new possibilities for communication and engagement as well as new avenues for control. Calls and texts can be ignored, and our phones are often used as shields. Busying ourselves with them can help us avoid unwanted face-to-face conversations. There are a number of critiques regarding the pressure of always-on contact, and a growing body of research that examines how users negotiate these demands. By examining group messaging, Mannell highlights how the boundaries of these chats are porous and flexible and mark a distinct communicative break from previous forms of mobile messaging, which were largely didactic. The advent of group chats has also led to an increasing complication of conversation boundaries. One group chat may have several strands of conversation sporadically re-engaged with over time. Manell's examination of group chats empirically illustrates the complexity of digitally-mediated conversations as they move across private, parochial, and public spaces in a way that is not necessarily temporally linear. Further research highlights the networked nature of digitally mediated interpersonal communication and how conversations sprawl across multiple platforms (Burchell). Couldry (16, 17) describes this complex web as the media manifold. This concept encompasses the networked platforms that comprise it and refers to its embeddedness in daily life. As we no longer “log on” to the internet to send and receive email, the manifold is both everywhere and nowhere; so too are our conversations. Gershon has described the ways we navigate the communicative affordances of these platforms as “media ideologies" which are the "beliefs, attitudes, and strategies about the media they [individuals] use" (391). Media ideologies also contain implicit assumptions about which platforms are best for delivering which kinds of messages. Similarly, Burchell argues that the relational ordering of available media technologies is "highly idiosyncratic" (418). Burchell contends that this idiosyncratic ordering is interdependent and relational, and that norms about what to do when are both assumed by individuals and learnt in their engagement with others (418). The influence of others allows us to adjust our practices, or as Burchell argues, "to attune and regulate one's own conduct … and facilitate engagement despite the diverse media practices of others" (418). In this model, individuals are constantly learning and renegotiating norms of conversation on a case by case, platform by platform basis. However, I argue that it is more illuminating to consider how we have collectively developed an implicit and unconscious set of norms and signals that govern our (collective) conduct, as digitally mediated conversation has become embedded in our daily lives. This is not to say that everyone has the same conversational skill level, but rather that we have developed a common toolbox for understanding the ebb and flow of digitally mediated conversations across platforms. However, these norms are implicit, and we only have a partial understanding of how they are socially achieved in digitally-mediated conversation. What Lies Beneath Most of what we do online is assumed not to be publicly visible. While companies like Facebook trace us across the web and peer into every nook and cranny of our private use patterns, researchers have remained focussed on what lies above in the light, not below, in the dark. This has meant an overwhelming focus on single platform studies that rely on the massification of data as a default measure for analysing sentiment and behaviour online. Sociologically, we know that what occurs in dark social spaces, or backstage, is just as important to social life as what happens in front of an audience (Goffman). Goffman's research uses the metaphor of the theatre to analyse how social life is accomplished as a performance. He highlights that (darkened) backstage spaces are those where we can relax, drop our front, and reveal parts of our (social) self that may be unpalatable to a broader audience. Simply, the public data accessible to researchers on social media are “trace data”, or “trace conversation”, from the places where conversations briefly leave (public) footprints and can be tracked and traced before vanishing again. Alternatively, we can visualise internet researchers as swabbing door handles for trace evidence, attempting to assemble a narrative out of a left-behind thread or a stray fingerprint. These public utterances, often scraped through API access, are only small parts of the richness of online conversation. Conversations weave across multiple platforms, yet single platforms are focussed on, bracketing off their leaky edges in favour of certainty. We know the social rules of platforms, but less about the rules between platforms, and in their darker spaces. Conversations briefly emerge into the light, only to disappear again. Without understanding how conversation is achieved and how it expands and contracts and weaves in and out of the present, we are only ever guessing about the social dynamics of mediated conversation as they shift between light, dark, and shadow spaces. Small things can cast large shadows; something that looms large may be deceptively small. Online they could be sociality distorted by disinformation campaigns or swarms of social bots. Capturing the Unseen: An Ethnomethodological Approach Not all data are measurable, computable, and controllable. There is uncertainty beyond what computational logics can achieve. Nooks and crannies of sociality exist beyond the purview of computable data. This suggests that we can apply pre-digital social research methods to capture these “below the surface” conversations and understand their logics. Sociologists have long understood that conversation is a social accomplishment. In the 1960s,

Natural language processing (NLP) has been changed by neural networks, which make it possible to analyze social media data in more detail, even though it is very large, not organized, and changes all the time. This abstract looks at how neural networks can be used in natural language processing (NLP) to analyze social media. Every day, social media sites produce huge amounts of written content that covers a wide range of topics, feelings, and writing styles. The subtleties and complexity of this data are often too much for traditional NLP methods to handle. Neural networks, on the other hand, offer strong answers because they can learn complex patterns and models from raw text. Sentiment analysis is one of the main ways that neural networks are used in social media research. Deep learning designs like recurrent neural networks (RNNs) or more advanced models like transformer-based architectures (e.g., BERT, GPT) can correctly describe how people feel about social media posts. Businesses can use this feature to find out what the public thinks, keep track of how people feel about their brand, and spot new trends in real time. Neural networks are useful for more than just analyzing mood. They can also help with named entity recognition (NER), topic modeling, and even figuring out humor and irony in text, which is hard for traditional rule-based systems because they rely on set rules and definitions. Also, neural networks are great at processing data in more than one language, which is important because social media is used all over the world. When learned on big datasets, models can be used across languages, giving us information about different language groups without a lot of human tweaking. Another big benefit of neural network methods is that they can be scaled up or down, which is very important for handling the huge amounts of data that social media sites create.

The growing prominence of social media (SM) influencers as key content creators on SM platforms highlights the need for a nuanced understanding of factors that drive follower engagement. Taking a sentiment-theoretic perspective, we examine the interplay between image and text sentiment in multimodal SM posts to show that alignment with followers’ sentiment consistency expectations is crucial for enhanced follower engagement with influencers’ SM posts. Drawing on expectation disconfirmation and negativity bias theories, we explore the impact of sentiment divergence in multimodal SM posts and the moderating role of the follower community’s duality tolerance on follower engagement. Analyzing a dataset of 24,000 Instagram posts, our findings suggest that sentiment divergence between the image and text within the same SM post can reduce follower engagement. Specifically, when the text sentiment is less positive than the image sentiment (negative divergence), the detrimental impact on engagement is notably more pronounced, while posts where the text sentiment is more positive than the image sentiment (positive divergence) appear to be less affected. Additionally, we find that higher duality tolerance within an influencer’s community mitigates the negative effects of sentiment divergence on follower engagement. Our research contributes to the SM literature by highlighting the importance of sentiment divergence for SM influencers and their SM community’s duality tolerance orientation when they are designing posts. We further provide practical insights on how to craft multimodal SM posts that are effective in enhancing follower engagement.

As the popularity and diversity of social media platforms increases so does their utility for health research. Using social media for recruitment into clinical studies and/or delivering health behavior interventions may increase reach to a broader audience. However, evidence supporting the efficacy of these approaches is limited, and key questions remain with respect to optimal benchmarks, intervention development and methodology, participant engagement, informed consent, privacy, and data management. Little methodological guidance is available to researchers interested in using social media for health research. In this Tutorial, we summarize the content of the 2017 Society for Behavioral Medicine Pre-Conference Course entitled ‘Using Social Media for Research,’ at which the authors presented their experiences with methodological and ethical issues relating to social media-enabled research recruitment and intervention delivery. We identify common pitfalls and provide recommendations for recruitment and intervention via social media. We also discuss the ethical and responsible conduct of research using social media for each of these purposes.

This perspective article suggests considering the everyday research data management work required to accomplish social media research along different phases in a data lifecycle to inform the ongoing discussion of social media research data’s quality and validity. Our perspective is informed by practical experience of archiving social media data, by results from a series of qualitative interviews with social media researchers, as well as by recent literature in the field. We emphasize how social media researchers are entangled in complexities between social media platform providers, social media users, other actors, as well as legal and ethical frameworks, that all affect their everyday research practices. Research design decisions are made iteratively at different stages, involving many decisions that may potentially impact the quality of research. We show that these decisions are often hidden, but that making them visible allows us to better understand what drives social media research into specific directions. Consequently, we argue that untangling and documenting choices during the research lifecycle, especially when researchers pursue specific approaches and may have actively decided against others (often due to external factors) is necessary and will help to spot and address structural challenges in the social media research ecosystem that go beyond critiques of individual opportunistic approaches to easily accessible data.

The prevalence of social media platforms that share photos and videos could prove useful for wildlife research and conservation programs. When social media users post pictures and videos of animals, near real-time data like individual identification, sex, location, or other information are made accessible to scientists. These data can help inform researchers about animal occurrence, behavior, or threats to survival. The endangered Hawaiian monk seal (Neomonachus schauinslandi) population has only 1,400 seals remaining in the wild. A small but growing population of seals has recently reestablished itself in the human-populated main Hawaiian Islands. While this population growth raises concerns about human-seal interactions it also provides the opportunity to capitalize on human observations to enhance research and conservation activities. We measured the potential utility of non-traditional data sources, in this case Instagram, to supplement current population monitoring of monk seals in the main Hawaiian Islands. We tracked all Instagram posts with the identifier #monkseal for a one-year period and assessed the photos for biological and geographical information, behavioral concerns, human disturbance and public perceptions. Social media posts were less likely to provide images suitable for individual seal identification (16.5%) than traditional sighting reports (79.9%). However, social media enhanced the ability to detect human-seal interactions or animal disturbances: 22.1%, of the 2,392 Instagram posts examined showed people within 3 meters of a seal, and 17.8% indicated a disturbance to the animal, meanwhile only 0.64% of traditional reports noted a disturbance to the animal. This project demonstrated that data obtained through social media posts have value to monk seal research and management strategies beyond traditional data collection, and further development of social media platforms as data resources is warranted. Many conservation programs may benefit from similar work using social media to supplement the research and conservation activities they are undertaking.

What prevails in the today’s research on social media is a functional view of technology. Technology is regarded as a set of technical devices used to conduct specific social functions, such as personal communication, social networking, public posting, and corporate advertising, among others. This paper proposes that such a functional view of technology renders social media research unduly limited and constrained in its scope, level, and direction of inquiry. Problematizing on some representative social media research efforts in the field of IS, this paper provides an alternative perspective, that is, to view social media as a technology-for- being that exerts a deeper level of influence on our existence, molding and shaping the nature and mode of being itself. Such a technology- for-being perspective has been rarely explored or subscribed to in the present IS social media research. Building upon the new conception of social media as a technology-for-being, this essay explores the quality of being in the context of social media. Five such qualities are discussed, including virtuality, materiality, externality, liquidity, and hybridity. The essay also explores the deep structural problems of research to guide future social media research. Six of such problems include Problematize-the-Natural, Follow- the-Actor, Welcomethe- Frankenstein, Weber-meets-Frankenstein, Freud-meets-Frankenstein, and Marx- meets-Frankenstein. The essay concludes with discussions on the implications of the essay, its limitations, and suggestions for future work.

There has been a significant increase in using social media for academic research and there is an opportunity for human factors professionals to incorporate these platforms into their research. Social media platforms provide a rich space to study extant data on health information communication, behaviors, and impacts and to recruit study participants. In this session, panelists will discuss using social media to study health-related topics including health management, gender-based violence, disaster response, self-harm, patient ergonomics, and secondary impacts of the COVID-19 pandemic. They will share how they have collected and analyzed data and recruited study participants from social media platforms such as Twitter, Reddit, and Facebook. They will also speak to the benefits and challenges of as well as ethical implications for using social media for research. There will be space for a moderated discussion to identify ways social media can be leveraged for human factors research in health care.