Media ecology and artificial intelligence: A critical perspective on authorship and lessons learned from social media

Twitter Archives and the Challenges of "Big Social Data" for Media and Communication Research

Playing Conspiracy

Alts and Automediality: Compartmentalising the Self through Multiple Social Media Profiles

Artificial intelligence (henceforth, AI) is one of the most remarkable topics on social media platforms. The current study aims to investigate the representation of AI in Bill Gates’ social media content to uncover the hidden ideology of one of the most influential figures in the field of AI technology. Furthermore, critical discourse analysis (henceforth, CDA) examines the relationship between language, ideology, and power in various social and cultural contexts. The study aims to answer the following questions: 1- What are the lexical devices that are used to represent Artificial Intelligence (henceforth, AI) in Gates' social media content to construct the "self "and the "other"? 2- How is intertextaulity utilized in social media in terms of ideology and the construction of "self" and "other"? The researcher forms an eclectic model of CDA using Fairclough’s (2001) three-dimensional model and Van Dijk’s (1995) ideological square model. Based on the findings, the study concludes that the examination of lexical devices reveals the way the "self" represents his AI technology and its benefits to the world. While the representation of "other" is illustrated in the environment that surrounds AI technology.

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 provide opportunities for organizations to reach the largest amount of people by measuring the general perception of the consumer and knowing his/her feelings and some of his reactions towards brands and products. On the other hand, these organizations as well as people are keen to preserve the privacy of their data, which will only be achieved by using applications and techniques of artificial intelligence. This scientific paper aims to analyze and discuss the impact of using artificial intelligence applications with their various technologies on social media through the method of critical analysis and evaluation that was applied to previous recent studies that dealt with the impact of artificial intelligence on social media. Then, this paper conducted a critical review of these studies coming up with findings, conclusions, and special recommendations on the subject. This resulted in the fact that studies agree on the great role that artificial intelligence plays in social media platforms in terms of preserving the privacy of the user and organizations and in terms of marketing and increasing the profits of organizations. Moreover, companies that do marketing through social media using artificial intelligence have benefited three times more than other companies that do not. In addition, artificial intelligence contributes to the preservation and security of privacy and data of users and digital owners in social networking sites, and contributes to increasing the profits of companies that use marketing through social media platforms based on artificial intelligence techniques, as their revenues increased by 10%, costs decreased, and productivity and logistic networks improved. This study recommended working to develop the mechanism of artificial intelligence in social media platforms, conducting more studies on the contribution of artificial intelligence in increasing the revenues earned by social media platforms, reducing the costs of social media creation and management, and finally the necessity for the commitment of social media companies to apply artificial intelligence techniques to maintain user privacy.

Conspiracy

Indian Dermatologists Wield Technology to Combat COVID-19!

This study examines how international news outlets frame artificial intelligence (AI) discussions on social media in Spanish-speaking Latin America, highlighting social media's role in shaping perceptions and attitudes. Fuchs (2024:35) conceptualises social media as techno-social systems in which information and communication technologies enable and constrain human activities that create knowledge produced, distributed, and consumed in a dynamic and reflexive process that connects technological structures and human agency. The paper centres on how discourses in social media are communicated to audiences when content about AI is distributed on those platforms, looking at the narratives embedded in those posts. This exploratory research uses critical discourse analysis to analyse how selected regional outlets address AI outside the Global North. It focuses on how news stories are published on the X.com (formerly known as Twitter) accounts of CNN Español, BBC Mundo, Infobae, and Telesur, two outlets coming from the Global North and two outlets originating in the Global South. Despite the growing popularity of platforms like TikTok and Instagram, X.com still fulfils the role of the digital public sphere, where it remains a popular social media application in Latin America with 57.5 million users (Statista, 2024). Using Norman Fairclough's three-dimensional model of critical discourse analysis, textual analysis, discourse practice, and social practice, the initial results of over 300 tweets found that AI is often framed using metaphors of competition and transformation, portraying it as a geopolitical contest and an unstoppable societal force. AI is also personified with human-like qualities, which can obscure the human and corporate influences behind its development. In addition, the study highlights distinctive patterns in how AI content is produced and consumed across social media platforms, where it uses expert opinions to legitimise its content. Finally, it reflects on how the editorial approaches of the selected outlets demonstrate how institutional ideologies and power structures influence the framing of AI. These findings contribute to the knowledge about journalism discourses and AI from the perspective of the Global South.

Using Digital Media to Promote Kidney Disease Education

The ‘toxic turn’ in social media platforms continues unabated. Hate speech, mis- and disinformation, misogynistic and racist speech, images, memes and videos are all far too common on social media platforms and more broadly on the internet. While the diminishing popularity of populist politicians led to hopes for less social toxicity, the Covid-19 pandemic introduced new and more complex dimensions. Tensions have emerged around what constitutes problematic content and who gets to define it. Co-regulation models, such as for example the EC Code of Conduct against Illegal Hate Speech, focus on the legality of certain types of contents, while leaving other categories of problematic contents to be defined by platforms. In parallel, the social media ecosystem became more diverse, as new platforms with hands off moderation policies attracted users who felt too constrained by the policies of mainstream platforms. The proposed panel examines this complex and dynamic landscape by problematizing what is understood as toxic, deplatformed, removable and in general problematic content on platforms with the aim to probe the boundaries of what is constituted as acceptable discourse on platforms and to map its implications. In particular, this panel discusses the broad definition of ‘problematic content’ employed by social media platforms, a catch-all term that cuts across hate speech and propaganda, including more politically topical content such as mal-, mis-, and disinformation, hyperpartisan and polarising content, but also abusive, misogynistic, racist, and homophobic discourse. The term is also employed to refer to spam and content that infringes upon the Terms of Service or the Community Standards of social media platforms. As such, it is a broad category that resists a narrower classification given the operational scope of its use. Defining what constitutes problematic content is a key operation of platform content moderation policies but is also the subject of intense debates (de Gregorio, 2020; Gillespie, 2018; Gillespie et al., 2020; Gorwa et al., 2020). The panel interrogates the many definitions and applications of problematic content on social media platforms and applications through an empirically informed lens and focusing on deleted contents, complex mixed narratives, and grey areas, including hidden misinformation on voice applications. Problematic Content according to Twitter Compliance API presents ongoing work on the Twitter Compliance API and the Compliance Firehose, which allow researchers to identify content that has been deleted, deactivated, protected, or suspended from Twitter, a proxy for problematic content. In Multi-Part Narratives on Telegram Siapera presents ongoing research that probes the intersection between Covid-19 scepticism, far right and other political narratives in vaccine hesitant groups on Telegram. The third contribution, What if Bill Gates really is evil, people? Investigating the infodemic’s grey areas discusses the conceptual and methodological definitions of problematic content in relation to work on anti-vax and other conspiratorial narratives on Instagram and on Twitter. The fourth contribution, Misinformation and other Harmful Content in Third-Party Voice Applications focuses on problematic content that is yet to be identified on voice applications such as personal assistants. The article addresses the methodological challenges of identifying and defining such contents on applications that have currently no content moderation policies. All contributions foreground the difficulties and costs of identifying and dealing with problematic contents on social media. The panel fits with theme of decolonization in two ways: firstly, because it is concerned with the tensions around how toxic/problematic contents are defined and who gets to define them; and secondly, because of its focus on neo-colonial discourses or justifications for colonialism in both narratives hosted by platforms and in platforms’ attempts to regulate content. As some narratives are flagged for removal by social platforms, they also raise the question of who is deciding and what does problematic content mean, with far right discourses exploiting this tension and ironically denouncing any attempt to regulate the public discourse as ideological enforcement and justification for (neo)colonial practices performed by social media platforms. From this perspective, platforms' own claims about what constitutes acceptable content is uncomfortably close to colonial narratives of civilised discourse and brings to the fore the potential for neo-colonial narratives and practices in digital spaces.

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

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.

Social Media Big Data: The Good, The Bad, and the Ugly (Un)truths.