Collaborative content generation on social media platforms: Social capital, team dynamics, and viewer engagement

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

This qualitative research study scrutinizes the use of animation and special effects in Indian advertising on social media platforms, aiming to shed light on their impact on viewer engagement, message recall, and brand recognition. The growing dominance of social media in modern marketing has led businesses to explore innovative ways to create engaging content that stands out amidst the clutter. Animation and special effects offer a promising avenue to achieve this goal by crafting visually captivating advertisements that effectively convey brand messages. The social media platforms are carefully chosen based on their expertise in designing and implementing animation and special effects. The primary focus of the analysis lies in understanding how animation and special effects are strategically employed to achieve specific marketing objectives. These recommendations underscore the importance of tailoring animation and special effects to suit the target audience's preferences and demographics. Furthermore, it advocates aligning campaign objectives with the chosen animation styles and content, ensuring a harmonious and cohesive message. By striking the right balance and synergy between creative elements and brand values, marketers can maximize the potential of animation and special effects to elevate their social media advertising efforts. This study provides valuable insights into the strategic use of animation and special effects in social media advertising. By understanding the nuances of these creative techniques and their impact on viewer engagement, marketers can make informed decisions when integrating them into their marketing strategies. The research serves as a valuable resource for shaping future marketing campaigns, fostering meaningful connections with target audiences, and ultimately achieving marketing success on social media platforms. As the landscape of social media and advertising continues to evolve, this study paves the way for further exploration into innovative visual storytelling and user-centric marketing approaches.

Engaging but inaccurate: A cross-sectional analysis of acne videos on social media from non–health care sources

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.

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.

The subject of this article is the use of Digital Human Technology (DHT) in live broadcasts on social media platforms and its impact on audience engagement and content appeal. This study examines how elements of DHT, such as virtual avatars and AI-driven hosts, are increasingly being used in live broadcasts to improve viewer engagement and retention. The main objective is to evaluate whether the integration of DHT increases viewer engagement, interactivity, and retention, especially compared to traditional streaming methods. This study consists of several key tasks: reviewing the current live DHT landscape, developing a research framework for analyzing engagement metrics, collecting empirical data through surveys and interviews and conducting statistical analysis to identify correlations between DHT use and viewer engagement. The methods used in this study include quantitative approaches such as structured questionnaires to measure viewership indicators and qualitative approaches such as in-depth interviews with streamers and viewers. Statistical methods, such as factor and correlation analysis, are used to assess the impact of DGT on key engagement metrics, such as viewing time, frequency of interaction, and viewer satisfaction. Through systematic observation, this study also captured real-time interactions, providing a comprehensive understanding of DHT effects. The findings emphasize that DHT significantly increases engagement in live broadcasts, providing content creators with innovative ways to retain audiences. However, the high cost of such technology and technical requirements limit its availability to independent streamers. This study provides practical recommendations for streamers and marketers that suggest DHT is a valuable tool for optimizing content appeal and audience engagement. Future research should explore scalable DHT solutions to improve accessibility for a wider range of content creators.

While hospitals’ health promotion via social media has the potential to be a critical source of health information, research shows racial and ethnic disparities exist in health-related knowledge that may be, in part, related to media representation. The purpose of this study is to examine the racial and ethnic representation of people featured in Washington, D.C. hospitals’ social media platforms to understand how hospitals embed cultural competency into their health communication. By comparing the diversity of images on hospitals’ social media platforms with the demographics of hospitals’ neighboring communities, the researchers intend to highlight opportunities to improve targeted health messaging to underserved communities, particularly Black and Hispanic communities. By analyzing the images and videos posted on the three most popular social media platforms – Facebook, Twitter, and YouTube – for a one-month period, the researchers found that Whites and Asians were over-represented while Hispanics were severely under-represented in hospitals’ social media representation as compared to the community demographics. Increasing the diversity of minority representation on hospitals’ social media-based health promotion may contribute to addressing the social disparities in healthcare.

With society evolving, social media platforms have become an indispensable component of individuals daily existence. However, the relationship between online trolling and user lurking behavior deserves more attention and research. This research, conducted using the Weibo platform, gathered 289 valid survey responses through a questionnaire to explore the correlation between online trolling and lurking. The moderating impacts of social media affordance and platform engagement were also examined. The results revealed that online trolling significantly and positively predicts lurking behavior; While the moderating influence of social media affordance did not yield significant results, the moderating impact of platform engagement was found to be significant. Specifically, at low levels of platform engagement, online trolling significantly and positively predicted lurking behavior, while at high levels of platform engagement, online trolling negatively predicted lurking behavior. Therefore, social media platforms should enhance their monitoring of online trolling and provide users with more privileges to increase their platform engagement and more features to deal with trolling, enabling them to participate in maintaining a healthy platform environment.

We conducted informal and formal trend analysis on the term ‘sunbed’ assessing its search popularity, identifying the top-three social media (SM) sites where the term is mentioned, characterizing the persons or groups most likely to post about sunbeds and analysing the content that is most frequently being made available in relation to sunbeds. We found a statistically significant increase in the search trend for the term ‘sunbed’ between 2008 and 2023. While Instagram had the most sunbed mentions, TikTok videos had more viewer engagement. TikTok had more influencer-driven sunbed posts than the other SM platforms. Commercial premises posting about sunbed use featured heavily on Instagram (56%) and TikTok (40%). Our study highlights the harmful influence SM can have.

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.

The advancement in Artificial Intelligence technology revolutionizes new opportunities and challenges, particularly with large language model ChatGPT, in various domains, especially in the educational platform. This research endeavors a comprehensive analysis to explore the concerns and expressions associated with this AI tool on the social media platform X and in academic contexts. Two distinct datasets, comprising X data and survey responses from academics, were utilized to achieve the objectives. This research examines the valuable concerns regarding ChatGPT among X users on social media platform. To implement the Natural Language Processing (NLP) techniques which included Sentiment Analysis and Topic Modeling using Latent Dirichlet Analysis (LDA), the study aimed to identify the significant insights expressed by the social media users. The analysis obtained that, most frequent discussed topic was “ChatGPT”. The majority of discussions among the X users were positive in sentiment (49%), focusing on the utility of ChatGPT. Comparatively, negative discussions (47%) were also expressed by the users (47%) about students’ cheating in exams, and the generation of inaccurate information, which could affect students’ learning skills, and their critical thinking. Furthermore, approximately 27% of the discussions were expressed neutral sentiment regarding the generation of contents by ChatGPT. Various machine learning models were implemented to predict the classification of sentiment labels correctly. The Random Forest model performed well to classify all the sentiment labels correctly compared to others with highest accuracy of 62%. This research also unveiled the academics’ opinion in the context of education. A case study was conducted among the academics, where approximately 59% reported using ChatGPT for academic purposes and academics (24%) use this tool occasionally. In terms of its usefulness, 32% academics consider it is as useful, especially for generating writing contents. Additionally, 29% of them believed that this tool primarily improves students’ language and writing skills but they also expressed the concerns about overreliance potentially impacting their critical thinking and violating academic integrity. The major concerned keywords for academics include “research”, “accuracy of information”, and “critical thinking”, while for students, “academic integrity”, “critical thinking”, “risk”, “copy-paste”, and “creativity skills”. The majority of the sentiments regarding the concerns were negative for students (38%), and minority for academics (28%). Overall, academics expressed positive sentiments about the utility of using ChatGPT. This research highlights these findings and recommends further exploration of using this tool in educational practices with a focus on the identified concerns to guide future implementation.

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.

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.

PurposeThe purpose of this paper is to understand whether the companies most involved in communicating their responsible behaviour externally are those most active on the social media (SM) platform, with a philanthropic purpose rather than strictly aimed at economic aspects.Design/methodology/approachThe authors, first, assess firms’ efforts on the SM platform using the model proposed by Chung et al. (2014), and, second, the authors analyze the content of messages in order to verify what dimensions of the corporate social responsibility (CSR) they contain. A multivariate modelling has been performed in order to verify whether the wineries that take most care to communicate their responsible behaviour are those that are more involved in the management of Social Network. The wineries’ effort in SM platform was analyzed using the model proposed by Chung et al. (2014), which consider three dimensions named intensity, richness and responsiveness. In order to verify the relationship between the SM effort and their engagement in CSR initiatives, the Probit model has been utilized taking into consideration four CSR dimension (Green CSR, Ethical CSR, Community CSR and Cultural CSR).FindingsThe findings show that wineries most involved in corporate social responsibility initiatives and in the active communication of these initiatives on SM platforms are those that are most active on SM and in particular those that interact most with their web users, triggering in them some reactions that lead to the sharing of content and, therefore, having a significant impact on the dissemination of information through SM.Research limitations/implicationsThe main limitations of this study are related to the limited sample size, the time period considered.Practical implicationsThis study provides insight and hints into wine entrepreneurs interested in improving the effectiveness of their CSR communication via SM showing the importance of the interactive dimension of SM, in order to reduce scepticism and gain greater credibility on the market.Originality/valueThis study uses four dimensions of the companies’ SM efforts’ built on the basis of a number of variables that are more explicative of the SM engagement.