When Product Markets Become Collective Traps: The Case of Social Media

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.

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

Using Digital Media to Promote Kidney Disease Education

The social media landscape is constantly evolving; new platforms emerge, and existing platforms change their functionality. While a robust body of literature exists on the influences of social media on students’ academic outcomes, most studies have failed to differentiate between specific social media platforms. Further, most research in this field focuses on how one variable (e.g., time spent on social media per day) relates to students’ GPA, giving an incomplete picture of how social media relate to student outcomes. The current study aimed to (a) investigate the intricate relationship between social media usage, time spent on schoolwork, and academic performance in college students; (b) confirm the rise of TikTok use among college students; and (c) understand college students’ perceptions of how their major social media platform influences their academic performance. Data were collected from a sample of undergraduate students in the USA (n=306). While the time spent on social media was negatively correlated with GPA (r=-.16, p<.001); time spent on schoolwork had no effect on GPA (r=.03, p=.580). Further, the time allocated to social media usage positively correlated with the time devoted to schoolwork (r=.14, p=.020), suggesting the pervasiveness of social media multitasking among college students. TikTok was the most commonly used social media platform, particularly by women. However, while TikTok-favoring students were more likely to think their GPA would be higher were they off social media, their GPA was not significantly different from other users who favored Snapchat and Instagram as their primary social media platforms, implying a discrepancy between student perception and reality. This may be because the TikTok videos are very short, thus one may watch a high number of them in a row and assume they have spent a long time on the app, which may have not been the case.

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.

Beware: Patients increasingly purchasing medications via social media

Social media (SM) platforms allow users to communicate rapidly, exchange information, and create and share real-time content. Currently, 4.5 billion people use social media worldwide, making it an influential part of daily life. Beyond information sharing, social media facilitates communication, transfers information, and serves as a platform for advertising and shaping public opinion. Researchers analyse these aspects to understand and describe societal realities. The primary purpose of this paper is to analyse social media’s impact on global research. The research included an analysis of the most popular social platforms, considering the number of Web of Science (WoS) articles relating to them and the year in which the platform was established or the Monthly Active Users (MAU) factor. Data were collected based on the WoS database in the topic (which contains texts of title, abstract, author keywords, and Keywords Plus) of the articles, where phrases containing names of SM platforms were used. Quantitative research is a type of research that analyses data numerically to find relationships and statistical regularities of searched phrases. The impact of social media on the dissemination of research and findings was analysed based on the results of the study and also on the literature data. This research reveals a lack of correlation between the number of articles indexed in the WoS and the MAU of individual social media platforms. This observation raises an important question: do social media researchers focus on studying the platforms used by the majority, thereby providing a more accurate representation of current social dynamics? This article is helpful for researchers, policymakers, and social media platform developers seeking to understand the role of social media in shaping modern communication and public discourse. The most important finding of the paper is the low correlation between the number of SM users and the impact of social media platforms on learning, as exemplified by the Twitter (Note: Twitter was an American social networking service rebranded as X in 2023. As the period of data analysed in this paper covered the years up to 2022, the authors decided to stay with the name Twitter) platform, which is the 17th largest SM platform but is the 2nd (after Facebook) in implications for science.

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.

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.

BackgroundMental illness is a growing concern within many college campuses. Limited access to therapy resources, along with the fear of stigma, often prevents students from seeking help. Introducing supportive interventions, coping strategies, and mitigation programs might decrease the negative effects of mental illness among college students.ObjectiveMany college students find social support for a variety of needs through social media platforms. With the pervasive adoption of social media sites in college populations, in this study, we examine whether and how these platforms may help meet college students’ mental health needs.MethodsWe first conducted a survey among 101 students, followed by semistructured interviews (n=11), of a large public university in the southeast region of the United States to understand whether, to what extent, and how students appropriate social media platforms to suit their struggle with mental health concerns. The interviews were intended to provide comprehensive information on students’ attitudes and their perceived benefits and limitations of social media as platforms for mental health support.ResultsOur survey revealed that a large number of participating students (71/101, 70.3%) had recently experienced some form of stress, anxiety, or other mental health challenges related to college life. Half of them (52/101, 51.5%) also reported having appropriated some social media platforms for self-disclosure or help, indicating the pervasiveness of this practice. Through our interviews, we obtained deeper insights into these initial observations. We identified specific academic, personal, and social life stressors; motivations behind social media use for mental health needs; and specific platform affordances that helped or hindered this use.ConclusionsStudents recognized the benefits of social media in helping connect with peers on campus and promoting informal and candid disclosures. However, they argued against complete anonymity in platforms for mental health help and advocated the need for privacy and boundary regulation mechanisms in social media platforms supporting this use. Our findings bear implications for informing campus counseling efforts and in designing social media–based mental health support tools for college students.

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.

As new social media platforms emerge and the existing ones change in functionality, their impact on students may shift. The current study investigated the number of social media accounts held by college students, and examined the social media platforms students use most, time spent on social media, time spent on schoolwork, and any gender differences in these variables. Additionally, we sought to understand college students’ perceptions of the addictiveness of their major social media platform and detect possible discrepancies between their perception and reality (as measured by time spent on social media daily). A total of 306 participants were recruited from a research university in the U.S. Our results revealed that all participants had multiple active social media accounts (over 5 on average) and spent on average 3.8 hours on social media daily, while spending on average 3.7 hours on schoolwork daily. Interestingly, female participants spend more time on social media than males, as well as spent more time on schoolwork than their male counterparts, suggesting a strategy of compensation. As hypothesized, TikTok was the most popular social media platform, followed by Snapchat, Instagram, and others such as X (formerly Twitter), YouTube, and Facebook. Contrary to popular beliefs, while TikTok-favoring students were more likely to perceive that they were vulnerable to compulsive overconsumption, their time on social media per day was not significantly different from any other social media active users, F(3, 302) = 1.43, p = .23, suggesting a discrepancy between student perception and reality.

Navigating Social Media in #Ophthalmology

IntroductionA persistent desire to use social media platforms is an indication of social media addiction, which has an adverse impact on academic performance, mental health, and social connections. According to WHO statistics, the percentage of adolescents using social media increased from 7% in 2018 to 11% in 2022. Social media improves communication among college students, but it can also lead to misuse. Addiction affects 18.4% of students worldwide, with higher percentages in Asia. Around 36.9% of college students in India exhibit addiction-related behaviors that are connected to anxiety, eye strain, poor sleep, and decreased academic performance. Addiction trends are further influenced by location and gender disparities. In Tamil Nadu, social media is widely used; however, little is known regarding its consequences. The purpose of this study is to determine the prevalence of social media addiction and its related factors among college students in the Chengalpattu district.MethodologyA cross-sectional study was performed among students pursuing professional courses in the Chengalpattu district. A total of 320 participants were selected using simple random sampling. Data were collected using the Bergen Social Media Addiction Scale and the Rosenberg Self-Esteem Scale and analyzed using Statistical Package for Social Sciences (SPSS) version 25 (IBM Corp., Armonk, NY). Descriptive statistics were used to present data in the form of tables, and the p-value was calculated, and logistic regression analysis was performed.ResultsAmong 320 students, 253 (79.1%) were in the age group of 18-21. Of those, 116 (36.3%) were male and 204 (63.7%) were female. A total of 243 (75.9%) participants were from nuclear families and resided in cities. For at least one to four hours per day, the majority of them used Instagram as their primary social media channel, followed by YouTube and others. Of the participants, it was found that 165 (51.6%) had good self-esteem, 154 (48.4%) had low self-esteem, and 18 (5.6%) were addicted to social media. Among the participants who have been surveyed, 42 (13%) reported that their personal relationships were affected because of social media, and they were much more likely to experience relationship problems (adjusted odds ratio (AOR): 4.69, 95% confidence interval (CI): 1.18-7.54, p=0.027). Around 36 (11.3%) individuals with social media addiction said they used social media for more than three hours per day. People who spent more than three hours a day were substantially more likely to be addicted to social media than those who spent less than that time (AOR: 4.71, 95% CI: 1.12-7.14, p=0.023).ConclusionAccording to this study, college students are becoming more involved in social media, with adolescents being the most active users. Usage may be supported by towns and nuclear families. Long-term use is linked to addictive behaviors and difficult relationships, despite the low overall rates of addiction. Similar levels of self-esteem suggest different psychological effects, emphasizing the necessity of mental health awareness and support to promote better use.

From Café Culture to Tweets