Developing a social media firestorm scale: from conceptualization to AI-assisted validation

Lists and Social MediaLists have long been an ordering mechanism for computer-mediated social interaction. While far from being the first such mechanism, blogrolls offered an opportunity for bloggers to provide a list of their peers; the present generation of social media environments similarly provide lists of friends and followers. Where blogrolls and other earlier lists may have been user-generated, the social media lists of today are more likely to have been produced by the platforms themselves, and are of intrinsic value to the platform providers at least as much as to the users themselves; both Facebook and Twitter have highlighted the importance of their respective “social graphs” (their databases of user connections) as fundamental elements of their fledgling business models. This represents what Mejias describes as “nodocentrism,” which “renders all human interaction in terms of network dynamics (not just any network, but a digital network with a profit-driven infrastructure).”The communicative content of social media spaces is also frequently rendered in the form of lists. Famously, blogs are defined in the first place by their reverse-chronological listing of posts (Walker Rettberg), but the same is true for current social media platforms: Twitter, Facebook, and other social media platforms are inherently centred around an infinite, constantly updated and extended list of posts made by individual users and their connections.The concept of the list implies a certain degree of order, and the orderliness of content lists as provided through the latest generation of centralised social media platforms has also led to the development of more comprehensive and powerful, commercial as well as scholarly, research approaches to the study of social media. Using the example of Twitter, this article discusses the challenges of such “big data” research as it draws on the content lists provided by proprietary social media platforms.Twitter Archives for ResearchTwitter is a particularly useful source of social media data: using the Twitter API (the Application Programming Interface, which provides structured access to communication data in standardised formats) it is possible, with a little effort and sufficient technical resources, for researchers to gather very large archives of public tweets concerned with a particular topic, theme or event. Essentially, the API delivers very long lists of hundreds, thousands, or millions of tweets, and metadata about those tweets; such data can then be sliced, diced and visualised in a wide range of ways, in order to understand the dynamics of social media communication. Such research is frequently oriented around pre-existing research questions, but is typically conducted at unprecedented scale. The projects of media and communication researchers such as Papacharissi and de Fatima Oliveira, Wood and Baughman, or Lotan, et al.—to name just a handful of recent examples—rely fundamentally on Twitter datasets which now routinely comprise millions of tweets and associated metadata, collected according to a wide range of criteria. What is common to all such cases, however, is the need to make new methodological choices in the processing and analysis of such large datasets on mediated social interaction.Our own work is broadly concerned with understanding the role of social media in the contemporary media ecology, with a focus on the formation and dynamics of interest- and issues-based publics. We have mined and analysed large archives of Twitter data to understand contemporary crisis communication (Bruns et al), the role of social media in elections (Burgess and Bruns), and the nature of contemporary audience engagement with television entertainment and news media (Harrington, Highfield, and Bruns). Using a custom installation of the open source Twitter archiving tool yourTwapperkeeper, we capture and archive all the available tweets (and their associated metadata) containing a specified keyword (like “Olympics” or “dubstep”), name (Gillard, Bieber, Obama) or hashtag (#ausvotes, #royalwedding, #qldfloods). In their simplest form, such Twitter archives are commonly stored as delimited (e.g. comma- or tab-separated) text files, with each of the following values in a separate column: text: contents of the tweet itself, in 140 characters or less to_user_id: numerical ID of the tweet recipient (for @replies) from_user: screen name of the tweet sender id: numerical ID of the tweet itself from_user_id: numerical ID of the tweet sender iso_language_code: code (e.g. en, de, fr, ...) of the sender’s default language source: client software used to tweet (e.g. Web, Tweetdeck, ...) profile_image_url: URL of the tweet sender’s profile picture geo_type: format of the sender’s geographical coordinates geo_coordinates_0: first element of the geographical coordinates geo_coordinates_1: second element of the geographical coordinates created_at: tweet timestamp in human-readable format time: tweet timestamp as a numerical Unix timestampIn order to process the data, we typically run a number of our own scripts (written in the programming language Gawk) which manipulate or filter the records in various ways, and apply a series of temporal, qualitative and categorical metrics to the data, enabling us to discern patterns of activity over time, as well as to identify topics and themes, key actors, and the relations among them; in some circumstances we may also undertake further processes of filtering and close textual analysis of the content of the tweets. Network analysis (of the relationships among actors in a discussion; or among key themes) is undertaken using the open source application Gephi. While a detailed methodological discussion is beyond the scope of this article, further details and examples of our methods and tools for data analysis and visualisation, including copies of our Gawk scripts, are available on our comprehensive project website, Mapping Online Publics.In this article, we reflect on the technical, epistemological and political challenges of such uses of large-scale Twitter archives within media and communication studies research, positioning this work in the context of the phenomenon that Lev Manovich has called “big social data.” In doing so, we recognise that our empirical work on Twitter is concerned with a complex research site that is itself shaped by a complex range of human and non-human actors, within a dynamic, indeed volatile media ecology (Fuller), and using data collection and analysis methods that are in themselves deeply embedded in this ecology. “Big Social Data”As Manovich’s term implies, the Big Data paradigm has recently arrived in media, communication and cultural studies—significantly later than it did in the hard sciences, in more traditionally computational branches of social science, and perhaps even in the first wave of digital humanities research (which largely applied computational methods to pre-existing, historical “big data” corpora)—and this shift has been provoked in large part by the dramatic quantitative growth and apparently increased cultural importance of social media—hence, “big social data.” As Manovich puts it: For the first time, we can follow [the] imaginations, opinions, ideas, and feelings of hundreds of millions of people. We can see the images and the videos they create and comment on, monitor the conversations they are engaged in, read their blog posts and tweets, navigate their maps, listen to their track lists, and follow their trajectories in physical space. (Manovich 461) This moment has arrived in media, communication and cultural studies because of the increased scale of social media participation and the textual traces that this participation leaves behind—allowing researchers, equipped with digital tools and methods, to “study social and cultural processes and dynamics in new ways” (Manovich 461). However, and crucially for our purposes in this article, many of these scholarly possibilities would remain latent if it were not for the widespread availability of Open APIs for social software (including social media) platforms. APIs are technical specifications of how one software application should access another, thereby allowing the embedding or cross-publishing of social content across Websites (so that your tweets can appear in your Facebook timeline, for example), or allowing third-party developers to build additional applications on social media platforms (like the Twitter user ranking service Klout), while also allowing platform owners to impose de facto regulation on such third-party uses via the same code. While platform providers do not necessarily have scholarship in mind, the data access affordances of APIs are also available for research purposes. As Manovich notes, until very recently almost all truly “big data” approaches to social media research had been undertaken by computer scientists (464). But as part of a broader “computational turn” in the digital humanities (Berry), and because of the increased availability to non-specialists of data access and analysis tools, media, communication and cultural studies scholars are beginning to catch up. Many of the new, large-scale research projects examining the societal uses and impacts of social media—including our own—which have been initiated by various media, communication, and cultural studies research leaders around the world have begun their work by taking stock of, and often substantially extending through new development, the range of available tools and methods for data analysis. The research infrastructure developed by such projects, therefore, now reflects their own disciplinary backgrounds at least as much as it does the fundamental principles of computer science. In turn, such new and often experimental tools and methods necessarily also provoke new epistemological and methodological challenges. The Twitter API and Twitter ArchivesThe Open

Purpose: Overwhelmed by the huge rise in the number of social media (SM) platforms, B to B firms have been increasingly using multiple social media (SM) platforms to enhance their relationships with their customers. The purpose of this study is to investigate the influence of the competitive pressure to use SM on B to B firms use of multiple SM platforms, organization and individual SM competences and on relationship sales performance. Method: An online survey is implemented to collect data from B to B firms from different industries in an emerging market, i.e. Kuwait, to produce 152 usable questionnaires. Structural equation modeling is carried out using Smart PLS 3. Findings: The main findings show that competitive pressure to use SM fully influences relationship sales performance through individual social media competence. It also influences relationship sales performance through two mediations (1) organizational SM competence, (2) on a less important level, through the use of multiple SM platforms and organizational SM competence. Additionally, both organization and individual SM competence are found to significantly influence relationship sales performance. Implications: This study uncovers the complex mechanism through which competitive pressures to use social media influence both individual and organization social media competence and their relationship with their customers. It demonstrates that the use of multiple SM platforms significantly increases relationship sales performance, but this influence is weak. Therefore, top managers must choose the right number of SM platforms and design clear SM strategies. Originality: This study sheds light on the influence of competitive pressure to use SM on B to B firms’ relationships with their customers i.e. relationship sales performance. This coercive pressure could potentially spread B to B firms’ resources over a large number of SM and lead to poor SM presence. The study also emphasizes the role of top management in choosing the optimal combination of SM platforms and developing their organization SM competence.

Beware: Patients increasingly purchasing medications via social media

Using Digital Media to Promote Kidney Disease Education

Social media (SM) platforms have demonstrated their ability to facilitate knowledge sharing on the global scale. They are increasingly often employed in educational and humanitarian domains where, despite their general benefits, they expose challenges peculiar to these domains. Specifically, the research context of this thesis is directed by my participation in the Go-Lab European project and my collaboration with Medecins Sans Frontieres (MSF) where SM platforms were used extensively. In this thesis, we address four challenges regarding analytics, privacy, discovery, and delivery, aiming to answer corresponding four research questions. How to provide user-oriented analytics in knowledge sharing systems to support awareness and reflection? What privacy management interfaces and mechanisms are suitable for knowledge analytics and learning analytics? How to enable discovery of knowledge relevant to user interests? How to facilitate knowledge delivery into settings where Internet connectivity is limited or absent? Henceforward, we provide an overview of our results. Analytics. To enable awareness and reflection for an SM platform users, we propose the embedded contextual analytics model where the analytics is embedded into the interaction context and presents information relevant to that particular context. Also, we propose two general architectures materializing this model respectfully based on real-time analytical applications and a scalable analytic back-end. Using these architectures, we provided analytics services to the SM platform users. We conducted an evaluation with the users demonstrating that embedded contextual analytics was useful to support their awareness and reflection. Privacy. To address the privacy concerns associated with the recording, storage, and analysis of user interaction traces, we propose a novel agent-based privacy management model. Our proposal uses a metaphor of physical presence of a tracking agent in an interaction context making the platform user aware of the tracking and allows to manage the tracking policy in a way similar to the physical world. We have implemented the proposed privacy interface in an SM platform and obtained positive evaluation results with the users. Discovery. Due to a large number of content items stored in SM platforms, it can be challenging for the users to find relevant knowledge. Addressing this challenge, we propose an interactive recommender system based on user interests enabling discovery of relevant content and people. We have implemented the proposed recommender in an SM platform and conducted two evaluations with platform users. The evaluations demonstrated the ability of the approach to identify relevant user interests and to recommend relevant content. Delivery. At the moment of writing in 2016, near half of the world's population still does not have reliable Internet access. Often, the places where humanitarian action is needed have limited Internet connection. We propose a novel knowledge delivery model that relies on a peer-to-peer middleware and uses low-cost computers for local knowledge replication. We have developed a system implementing the model and evaluated it during eight deployments in MSF missions. The evaluation demonstrated its knowledge delivery abilities and its usefulness for the field staff.

Guidance for Professional Use of Social Media in Nutrition and Dietetics Practice

To determine which social media analytics tools, techniques, and platforms were developed in recent times, this paper reviews tools, techniques, and platforms related to social media analytics. In this paper, we talk about the tools used to deal with various social media data (social networking, media, etc.). In the past decade, there has been advancement in the technologies used to deal with social media as there has been an increase in the number of people using social media to share information and also the development of the new social media platforms that have let to increase in the amount of data that we have to deal with. Social media platforms have a considerable number of users across the world, which is overgrowing. These people are sharing information through these sources. There is a large quantity of social data comprising of data related to users, videos, web-based relations, and interactions, etc. which needs to be analyzed. Therefore analyzing social media data has become a significant activity for researchers, mainly due to the availability of the web-based API from social media platforms like twitter, facebook [1], Gmail, etc. This has also led to the development of data services, software tools for analyzing social media data. In this paper, there is a detailed review of the leading software tools and techniques that are used for scraping, cleaning, and analyzing social media data.

Driven by the need to enhance user traffic on social media (SM) platforms for increasing their advertising revenues, SM platforms are experimenting with new content creation features. However, it is unclear if such initiatives are also beneficial for SM profile owners such as influencers, who are the prime content creators on the SM platforms who use SM posts to build their influence within their network of followers. Our study investigates the effect of introducing one such new SM feature: the “story” on the creation and consumption of SM posts. Leveraging social penetration theory, we hypothesize the influence of introducing story feature on (1) the frequency of SM post creation by profile owners and (2) the extent of follower engagement with SM posts. Employing a quasi-experimental design, we find that the introduction of the story feature reduces the frequency of SM post creation, but the enhanced self-disclosure through the story feature increases follower engagement with the SM posts. However, these effects are moderated by the situating culture of the SM communities: while low-power-distance cultures value profile owners’ self-disclosure, high-power-distance cultures exhibit a mixed influence. Advancing literature on social penetration theory and SM user engagement, our study demonstrates that new self-disclosive SM content creation features do not necessarily benefit all the concerned stakeholders and that the effectiveness of such features might vary from one community to another. Hence, the intended impact of introducing new SM features needs to be carefully evaluated by SM platforms in a holistic manner.

In recent years Social Media (SM) platforms are becoming highly significant for the tourism industry as a medium for information exchange and communication platforms for tourists and travelers. Tourists are using Web 2.0 platforms to plan their travel, book hotels, confirm and cancel reservations, enquire about packages and itineraries, to read reviews posted by other travelers, and also to share their travel experiences by posting reviews, comments, ratings, photographs, etc. with others. The purpose of this study is to determine the influence of user-generated-contents on social media platforms in the travel planning of tourists in Udaipur, India. This study analyze the opinion of tourists regarding the benefits of social media and travel material posted on various social media platforms and to draw factors that are helpful in influencing the use of information through social media. To fulfill the objectives, primary data was collected by using a judgmental sampling method and a 5-point Likert type scale through a structured questionnaire. A sample of 309 respondents who visited Udaipur as a tourist during the period of early October 2017 to the end of March 2018 was surveyed. Using descriptive statistics and factor analysis results were presented and explained. The findings revealed that tourists have a positive opinion towards online reviews and travel material posted on social sites. The majority of the tourist respondents opined that online reviews, ratings, and comments, etc. regarding travel destinations, hotels, food, and climate, etc. help in their travel planning and travel related decisions. The results of factor analyses demonstrated that three factors namely; social media ease and trust, social media risk reduction and helpfulness and social media enhance joy and excitement were considered helpful in influencing the use of information through social media sites.

From Café Culture to Tweets

“I Need You All to Understand How Pervasive This Issue Is”: User Efforts to Regulate Child Sexual Offending on Social Media

The depiction of alcohol is the focus of a growing number of content analyses in the field of social media research. Typically, the occurrence and nature of alcohol representations are coded to measure the prevalence, normalization, or even glorification of alcohol and its consumption on different social media platforms (Moreno et al., 2016; Westgate & Holliday, 2016) and smartphone apps (Ghassemlou et al., 2020). But social media platforms and smartphone apps also play a role in the prevention of alcohol abuse when they disseminate messages about alcohol risks and foster harm reduction, abstinence, and sobriety (Davey, 2021; Döring & Holz, 2021; Tamersoy et al., 2015; Westgate & Holliday, 2016). Field of application/theoretical foundation: Social Cognitive Theory (SCT; Bandura 1986, 2009) as the dominant media effects theory in communication science, is applicable and widely applied to social media representations of alcohol: According to SCT, positive media portayals of alcohol and attractive role models consuming alcohol can influence the audience’s relation to alcohol. That’s why positive alcohol portayals in the media are considered a public health threat as they can foster increased and risky alcohol consumption among media users in general and young people in particular. The negative health impact predicted by SCT depends on different aspects of alcohol portrayals on social media that have been traditionally coded in manual content analyses (Beullens & Schepers, 2013; Mayrhofer & Naderer, 2019; Moreno et al., 2010) and most recently by studies relying on computational methods for content analysis (e.g. Ricard & Hassanpour, 2021). Core aspects of alcohol representations on social media are: a) the type of communicator / creator of alcohol-related social media content, b) the overall valence of the alcohol portrayal, c) the people consuming alcohol, d) the alcohol consumption behaviors, e) the social contexts of alcohol consumption, f) the types and brands of consumed alcohol, g) the consequences of alcohol consumption, and h) alcohol-related consumer protection messages in alcohol marketing (Moreno et al., 2016; Westgate & Holliday, 2016). For example, a normalizing portrayal shows alcohol consumption as a regular and normal behavior of diverse people in different contexts, while a glorifying portrayal shows alcohol consumption as a behavior that is strongly related to positive effects such as having fun, enjoying social community, feeling sexy, happy, and carefree (Griffiths & Casswell, 2011). While criticism of glorifying alcohol portrayals in entertainment media (e.g., music videos; Cranwell et al., 2015), television (e.g., Barker et al., 2021), and advertising (e.g., Curtis et al., 2018; Stautz et al., 2016) has a long tradition, the concern about alcohol representations on social media is relatively new and entails the phenomenon of alcohol brands and social media influencers marketing alcohol (Critchlow & Moodie, 2022; Turnwald et al., 2022) as well as ordinary social media users providing alcohol-related self-presentations (e.g., showing themselves partying and drinking; Boyle et al., 2016). Such alcohol-related self-presentations might elicit even stronger identification and imitation effects among social media audiences compared to regular advertising (Griffiths & Casswell, 2011). Because of its psychological and health impact, alcohol-related social media content – and alcohol marketing in particular – is also an issue of legal regulation. The World Health Organization states that “Europe is the heaviest-drinking region in the world” and strongly advocates for bans or at least stricter regulations of alcohol marketing both offline and online (WHO, 2020, p. 1). At the same time, the WHO points to the problem of clearly differentiating between alcohol marketing and other types of alcohol representations on social media. Apart from normalizing and glorifying alcohol portayals, there are also anti-alcohol posts and comments on social media. They usually point to the health risks of alcohol consumption and the dangers of alcohol addiction and, hence, try to foster harm reduction, abstincence and sobriety. While such negative alcohol portayals populate different social media platforms, an in-depth investigation of the spread, scope and content of anti-alcohol messages on social media is largely missing (Davey, 2021; Döring & Holz, 2021; Tamersoy et al., 2015). References/combination with other methods of data collection: Manual and computational content analyses of alcohol representations on social media platforms can be complemented by qualitative interview and quantitative survey data addressing alcohol-related beliefs and behaviors collected from social media users who a) create and publish alcohol-related social media content and/or b) are exposed to or actively search for and follow alcohol-related social media content (e.g., Ricard & Hassanpour, 2021; Strowger & Braitman, 2022). Furthermore, experimental studies are helpful to directly measure how different alcohol-related social media posts and comments are perceived and evaluated by recipients and if and how they can affect their alcohol-related thoughts, feelings, and behaviors (Noel, 2021). Such social media experiments can build on respective mass media experiments (e.g., Mayrhofer & Naderer, 2019). Insights from content analyses help to select or create appropriate stimuli for such experiments. Last but not least, to evaluate the effectiveness of alcohol marketing regulations, social media content analyses conducted within a longitudinal or trend study design (including measurements before and after new regulations came into effect) should be preferred over cross-sectional studies (e.g., Chapoton et al., 2020). Example Studies for Manual Content Analyses: Coding Material Measure Operationalization (excerpt) Reliability Source a) Creators of alcohol-related social media content Extensive explorations on Facebook, Instagram and TikTok Creators of alcohol-related social media content on Facebook, Instagram and TikTok Polytomous variable “Type of content creator” (1: alcohol industry; 2: media organization/media professional; 3: health organization/health professional; 4: social media influencer; 5: ordinary social media user; 6: other) Not available Döring & Tröger (2018) Döring & Holz (2021) b) Valence of alcohol-related social media content N = 3 015 Facebook comments N = 100 TikTok videos Valence of alcohol-related social media content (posts or comments) Binary variable “Valence of alcohol-related social media content” (1: positive/pro-alcohol sentiment; 2: negative/anti-alcohol sentiment) Cohen’s Kappa average of .72 for all alcohol-related variables in codebook* Döring & Holz (2021) *Russell et al. (2021) c) People consuming alcohol N = 160 Facebook profiles (profile pictures, personal photos, and text) Portrayal of people consuming alcohol on Facebook profiles Binary variable “Number of persons on picture” (1: alone; 2: with others) Cohen’s Kappa > .90 Beullens & Schepers (2013) d) Alcohol consumption behaviors N = 160 Facebook profiles (profile pictures, personal photos, and text) Type of depicted alcohol use/consumption Polytomous variable “Type of depicted alcohol use/consumption” (1: explicit use such as depiction of person drinking alcohol; 2: implicit use such as depiction of alcohol bottle on table; 3: alcohol logo only) Cohen’s Kappa = .89 Beullens & Schepers (2013) N = 100 TikTok videos Multiple alcoholic drinks consumed per person Binary variable “Multiple alcoholic drinks consumed per person” as opposed to having only one drink or no drink per person (1: present; 2: not present) Cohen’s Kappa average of .72 for all alcohol-related variables in codebook Russell et al. (2021) N = 100 TikTok videos Alcohol intoxication Binary variable “Alcohol intoxication” (1: present; 2: not present) Cohen’s Kappa average of .72 for all alcohol-related variables in codebook Russell et al. (2021) N = 4 800 alcohol-related Tweets Alcohol mentioned in combination with other substance use Binary variable “Alcohol mentioned in combination with tobacco, marijuana, or other drugs” (1: yes; 2: no) Cohen’s Kappa median of .73 for all pro-drinking variables in codebook Cavazos-Rehg et al. (2015) e) Social contexts of alcohol consumption N = 192 Facebook and Instagram profiles (profile pictures, personal photos, and text) Portrayal of social evaluative contexts of alcohol consumption on Facebook and Instagram profiles Polytomous variable “Social evaluative context” (1: negative context such as someone looking disapprovingly at a drunk person; 2: neutral context such as no explicit judgment or emotion is shown; 3: positive context such as people laughing and toasting with alcoholic drinks) Cohen’s Kappa ranging from .68 to .91 for all variables in codebook Hendriks et al. (2018), based on previous work by Beullens & Schepers (2013) N = 51 episodes with a total of N = 1 895 scenes of the American adolescent drama series “The OC” Portrayal of situational contexts of alcohol consumption in scenes of a TV series Polytomous variable “Setting of alcohol consumption” (1: at home; 2: at adult / youth party; 3: in a bar; 4: at work; 5: at other public place) Polytomous variable “Reason of alcohol consumption” (1: celebrating/partying; 2: habit; 3: stress relief; 4: social facilitation) Cohen’s Kappa for setting of alcohol consumption .90 Cohen’s Kappa for reason of alcohol consumption .71 Van den Bulck et al. (2008) f) Types and brands of consumed alcohol N = 17 800 posts of Instagram influencers and related comments Portrayal of different alcohol types and alcohol brands in Instagram posts Polytom

The Importance of Informative Interventions in a Wicked Environment

Navigating Social Media in #Ophthalmology

The burgeoning online gambling industry has begun to use social media platforms as a new way to capture the interest of clientele. Betting directly on social media platforms is an illegal act in almost all of the United States; however, no studies have assessed how the frequency of engaging in this illegal form of gambling may be predictive of problematic gambling. The objective of this survey study was to determine the prevalence of gambling on social media and its relationship to problem gambling. A representative online national sample of 17,767 U.S. adults was collected in February 2025. Of these, 12,845 (72%) reported some lifetime gambling, and they were surveyed on a variety of gambling behaviors, including their direct participation in betting on social media websites. The survey also included an assessment of problem gambling using the Short-form South Oaks Gambling Scale. Nearly 24% of lifetime gamblers reported using social media platforms to place wagers, with 14% reporting having done so in the past year. Past-year direct gambling on social media was associated with greater relative odds of problematic gambling (aOR=2.3, CI:2.0- 2.7, p<.001) when sociodemographic variables were statistically considered. There were some distinctions between various social media platforms relating to the risk of problematic gambling, with TikTok showing the greatest relative risk (aOR=2.9, CI:2.2, 3.8, p<.001). Direct gambling on social platforms is common among those who gamble and is strongly associated with the increased odds of pathological gambling. More detailed information on the risks associated with each of the various social media platforms may be helpful for policy makers and regulators as they endeavor to limit the destructive consequences of online gambling.