Exploring the Consumption of Recreational Fishing Content on Social Media

Using Digital Media to Promote Kidney Disease Education

This chapter examines the structure and sentiment of the Twitter response to Nathan Broad's naming as the originator of an image-based sexual abuse incident following the 2017 Australian Football League Grand Final. Employing Social Network Analysis to visualize the hierarchy of Twitter users responding to the incident and Applied Thematic Analysis to trace the diffusion of differing streams of sentiment within this hierarchy, we produced a representation of participatory social media engagement in the context of image-based sexual abuse. Following two streams of findings, a model of social media user engagement was established that hierarchized the interplay between institutional and personal Twitter users. In this model, it was observed that the Broad incident generated sympathetic and compassionate discourses among an articulated network of social media users. This sentiment gradually diffused to institutional Twitter users – or Reference accounts – through the process of intermedia agenda-setting, whereby the narrative of terrestrial media accounts was altered by personal Twitter users over time. Keywords Image-based sexual abuse Informal justice Social network analysis Technology facilitated violence Twitter Digital criminology Citation Broadbent, E. and Thompson, C. (2021), "Justice on the Digitized Field: Analyzing Online Responses to Technology-Facilitated Informal Justice through Social Network Analysis", Bailey, J., Flynn, A. and Henry, N. (Ed.) The Emerald International Handbook of Technology-Facilitated Violence and Abuse (Emerald Studies In Digital Crime, Technology and Social Harms), Emerald Publishing Limited, Bingley, pp. 689-709. https://doi.org/10.1108/978-1-83982-848-520211051 Publisher: Emerald Publishing Limited Copyright © 2021 Ella Broadbent and Chrissy Thompson. Published by Emerald Publishing Limited. This chapter is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of these chapters (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode. License This chapter is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of these chapters (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode. Introduction On the evening of September 30, 2017, the final siren sounded on the Australian Football League (AFL) season, and the Richmond Football Club (RFC) secured their first Premiership cup in 37 years. In the hours following the evening's celebrations, an image of a young woman with a premiership medal around her neck – her face cropped and breasts exposed – began to circulate through social networking platforms and fan forums. This image was quick to disperse through social media channels, becoming a symbol for the victory celebrations of the young men within the team and their army of supporters. It later emerged that the woman photographed had not consented to having the image shared. In the month after this incident, a formal police inquiry was conducted at the request of the victim to protect her anonymity, leading to a gradual reduction of the image's appearance within social and terrestrial media. Following the closure of this investigation, the victim's lawyers issued a statement maintaining that while the image had been taken with her consent, she was under the impression that it had been deleted shortly after – and certainly not distributed via social media (Maurice Blackburn Lawyers, 2017). She sought to drop the charges police laid against the accused both to protect her identity and to prevent further distress (SBS, 2017). On 29 October 2017 – almost a month after the image's release – premiership player Nathan Broad was identified as the person responsible for taking and distributing the original image. A press conference was held with Broad and the president of the RFC, Peggy O'Neal, where Broad issued a statement claiming he would take full responsibility for his actions and confirmed he took and distributed the image without consent (Cherny, 2017). The only formal sanction Broad received from the RFC was a three-week suspension at the beginning of the 2018 AFL season (Cherny, 2017). This incident (henceforth referred to as the “Broad incident”) 1 represents a high-profile case of a certain form of technology-facilitated violence (TFV) – image-based sexual abuse (IBSA). Due to police intervention, attempts were made to remove the original image from circulation on social media. While this image can still be located today, its circulation decreased significantly after police attempted to remove it. As a result of these efforts, the ability to analyze the diffusion of the original incident of nonconsensual sexual imagery was limited. However, the subsequent identification in late October 2017 of Nathan Broad as the individual who took the image generated significant social media interest, and produced a body of historical Twitter data that provided a valuable substrate for us to analyze how social media users sought to contest or condone Nathan Broad's naming and sanction. Incidents such as these hold value for criminological analysis, as they can provide a window into public sentiments on the perpetration of, and institutional responses to, IBSA. Moreover, as criminologists such as Powell, Overington, and Hamilton (2018) have demonstrated, examining responses to high-profile crimes on social media can provide a valuable means of researching the content and diffusion of narratives about crime in the contemporary mediascape. In undertaking such projects, a Social Network Analysis (SNA) methodology provides important insights into the degree of homophily within networks who respond to crimes on social media, and the role of central “nodes” in diffusing narratives about crime and perpetration. 2 Such insights are important given that early work within digital criminology has emphasized the “networked” morphology of contemporary harms – speaking, for example, of “networked misogyny” (Banet-Weiser & Miltner, 2016; Thompson & Wood, 2018, p. 12) – but has yet to examine the structural properties of these networks. To date, criminologists have yet to explore the utility of an SNA approach for examining social media–based responses to IBSA and other criminalized acts. To demonstrate the utility of SNA within this context, this study examined this incident of IBSA in Australia. In addition to answering the questions below, this project was concerned with not only the what of the research process but also the how. Readers are encouraged to consider the results and analysis as they would a pilot study – wherein qualitative Twitter data and a network of user relations were operationalized to generate an initial framework for understanding how structural analysis of incidents of IBSA can better demarcate the spread of sentiment through a network. In examining the Broad incident using this methodology, we sought to address the following questions: What are the structural and intermedia features of Twitter users' responses to IBSA? In what ways do Twitter users contest and/or condone IBSA? These questions enabled us to explore user engagement as a process involving both personal and media accounts, and the interactions they share to contest and/or condone a narrative on an incident of IBSA. Furthermore, our analysis offers a novel approach to analyzing public sentiment toward an incident of IBSA, providing structural and intermedia analysis of the response to the incident. This chapter is divided into four main sections. In section one, we review the relevant literature relating to online engagement, digital platforms, and IBSA. In the next section, we discuss the methodological contributions of this work and reflect on the utility of the methods used in this research for future studies into TFV and IBSA. In section three, we detail the major findings from this research; drawing on an Applied Thematic Analysis (ATA) we outline the narrative promulgated by both journalist and news media accounts, alongside personal Twitter users, and how interactions between these users generated a reverse-flow of sentiment. This is represented within a hierarchized visual framework characteristic of SNA to outline the flow of information and sentiment between different types of users responding to the Broad incident. This process enabled us to identify three “user roles” – Reference, Mediator, and Listener – with each performing distinct functions in the diffusion and reception of sentiment within the network. These user roles demonstrate how responses to crime on social media may be structured by clear hierarchies, with some users occupying dialogically significant positions within these networks. Through an ATA of the tweets responding to Broad's naming in the media, we identified an informal justice-seeking response by users to Broad and his actions, alongside a replication of this sentiment within Reference users over time. The counterhegemonic discourses that appeared in response to Broad disrupted the neutral narrative of the Broad incident within Reference accounts and produced a more favorable and balanced consideration of the harms to the victim. Finally, we conclude by reflecting on the implications of this research for online responses to IBSA. We posit that demarcating the connections between social media actors – Mediators, Listeners, and Reference accounts – may enhance our understanding of their specific role in contesting and altering passive narratives of sexual harm online. Literature Review This chapter is situated amid a burgeoning literature on the spectatorship of, and engagement with, TFV and IBSA (Henry, McGlynn, Flynn, Johnson, Powell, & Scott, 2020; Henry & Powell, 2018). It is important to note that such spectatorship involves not only individual observation of an incident of IBSA but also their response to this observation. Here, the act of spectatorship is not confined simply to the object and viewer, or “the spectator and the spectacle,” but also involves “the association between spectators,” or in this instance structural relations between Twitter users (Wood, 2017, p. 9). This grounds our methodology, in seeking to demarcate the connections that shape engagement on social media platforms, a process which is mediated not only by social forces but also the technological (infra)structure of social networking platforms. Twitter has been described as a “personal public” (Schmidt, 2014, p. 4) – a communicative space framed by the dimensions of software, relations, and rules. The concept of personal publics is not limited to Twitter and operates as a foundation for understanding the mechanics of Web 2.0 and user-generated content and interactions. Within a personal public, information is selected and displayed according to personal relevance criteria such as the social network a user situates themselves within. This is then mediated through ties made explicit by the platform – such as following, retweeting, and liking. Twitter itself can be distinguished from other social networks by the specific articulation of these user relations which are utilized to structure communicative flow – “the nexus of social ties and textual references, based on code-enabled connections” (Schmidt, 2014, p. 6). The foundational concept guiding these Twitter relations is that of “following” users – a unilateral relationship used to subscribe to other users' tweets and calculate user visibility metrics. Replies, retweets, and mentions, function as communicative references that allow for navigation to user profiles. These factors produce a stable and dynamic social networking service consisting of networked and distributed conversations (Schmidt, 2014), which enables potentially exponential public distribution and engagement with nonconsensual sexual imagery such as the image released by Broad. Researchers have noted that social media platforms such as Twitter have allowed for a redemocratization of the public sphere (Papacharissi, 2002). The assembly of counterpublics (Fraser, 1990) by girls and women on social media to contest social exclusion and subordination has been documented within criminological literature (Khoja-Moolji, 2015). Technology has, for example, allowed victims and their supporters to engage in “name and shame” tactics to ensure that behavior of abusers is not excused, and to contest the inadequacy of institutional responses to sexual violence such as TFV (Powell, 2015; Salter, 2013; Wood, Rose, & Thompson, 2019, p. 3). Considering the inadequacy of existing institutional responses to rape and violence against women in the form of state-sanctioned justice (Powell, 2015), social media–enabled informal justice-seeking plays an important role in the way victims of sexual violence and their supporters can create counterhegemonic discourses online. Informal justice and contemporary digital activism movements have been conceptualized as an asymmetrical and nonhierarchical endeavor within Powell, Stratton, and Cameron's (2018) theory of rhizomatic justice. Drawing on Deleuze and Guattari's (1988) figuration of the rhizome, Powell, Stratton et al.’s (2018) theory accounts for the diversity of activist behaviors in a digital context, where organizations, groups, and individuals are linked loosely and embody significant diversity (Funke, 2014). The resulting spread of motivations and agendas can lead these forms of activism to both enhance democracy and new forms of bigotry, which may outweigh the original infraction (Powell, Stratton et al., 2018). This is a constructive explanation for the consequences of informal justice-seeking. The utilization of a rhizomatic analogy, however, implies a flattening of hierarchies within these communities, which minimizes the “algorithmically-curated information environment” (Wood, 2019, p. 573) of social media, the in-built architecture that structures social media use. Identifying key nodes and influencers residing within a network who occupy a more significant role in information diffusion (Wood et al., 2019) holds the potential to identify information flow to institutional social media accounts. This chapter compliments the rhizomatic model of informal justice-seeking through an identification of the structural relations that underpinned online users' informal justice response to the Broad incident. Methodology Our study utilized a parallel mixed methods research design that combined a quantitative SNA and qualitative ATA (Borgatti, Everett, & Johnson, 2018) to establish a structural understanding of the social media response to the 2017 Broad incident. These methods enabled the assessment of differing components of the phenomenon, enhancing its interpretability. We collected and analyzed these quantitative and qualitative data sources separately before integrating them in the second phase of the project (Creswell & Clark, 2007). To begin this process, an SNA was conducted to develop a quantitative representation of different user types with a corpus of data relating to an incident of IBSA. SNA enabled the visualization of relational ties between social actors – in this case individual Twitter users, such as journalists, institutional news media accounts, and personal Twitter users. This included interactions such as liking or retweeting user content, and follower/followee relationships. These ties could be typologized through user type differentiation established by Beguerisse-Díaz, Garduno-Hernández, Vangelov, Yaliraki, and Barahona (2014) in their SNA of the London riots:

Government Surveillance and Counter-Surveillance on Social and Mobile Media: The Case of Iran (2009)

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

Beware: Patients increasingly purchasing medications via social media

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Social media may provide information for monitoring recreational fisheries, but several caveats prevent operationalization. Specifically, the fraction and profile of recreational fishers sharing their catches is not known. Our aim was to advance the monitoring capacities of recreational fishing using social media data. We collected data with onsite (face-to-face) survey and online (emails) questionnaires to characterize marine recreational fishers sharing catches on digital platforms (“sharers”) along with other demographic or fishing information. In the online survey we found that 38% of recreational fishers share their catches using digital platforms (including the private messaging platform WhatsApp), but such proportion dropped to 12% when considering only public or semi-public social media (Instagram was the most commonly used platform, followed by Facebook, YouTube, and Twitter). A similar pattern was found with the online questionnaire where sharers represented 37% of recreational fishers (including WhatsApp), while such proportion dropped to 21% when considering only public or semi-public social media. In general, sharers were more avid (24 and 35 yearly fishing trips for onsite and online survey, respectively) compared to non-sharers (18 and 31 yearly fishing trips). Sharers also spent more money on each fishing trip (on average 26 and 31 euro for onsite and online survey, respectively) than non-sharers (on average 21 and 28 euro for onsite and online survey, respectively), but they had similar chances of catching something. However, for fishers with catches, the harvest per unit effort of sharers was higher than that of non-sharers (0.4 and 0.5 kg/h with respect to 0.3 and 0.4 kg/h, for onsite and online survey, respectively). Moreover, recreational fishers that caught trophy, iconic, or emblematic species were more inclined to share their catches. This study represents an important advancement for integrating social media data into the monitoring of recreational fishing.

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

The purpose of this study is to investigate the influence of social and digital media platforms in improving teaching and learning practices in Indian schools. With the rapid growth of technology and the increasing distribution of internet access across the country, social and digital media platforms have emerged as powerful instruments with the potential to transform the educational environment. The benefits, constraints, and potential solutions for effectively leveraging these platforms to improve teaching and learning outcomes in Indian schools are examined in this study. The incorporation of social media platforms such as Facebook, Twitter, and Instagram allow teachers to develop virtual communities, facilitate communication, and engage students in dynamic discussions outside of the traditional classroom. These platforms facilitate cooperation, peer-to-peer learning, and the exchange of educational information. Furthermore, they allow teachers to deliver personalized feedback, monitor student progress, and assess learning outcomes in real-time, creating a more student-centered and inclusive learning environment. Digital media platforms, such as online learning management systems, educational apps, and interactive multimedia content, are critical for increasing access to quality education in remote locations and closing the digital divide. These platforms offer a plethora of instructional resources, ranging from e-books and videos to simulations and virtual reality experiences, all of which can help students better learn and retain complicated concepts. Furthermore, adaptive learning capabilities on digital media platforms adjust instructional content to individual students' requirements and learning styles, enabling personalized and self-paced learning experiences. However, integrating social and digital media platforms in Indian schools presents several problems.

Technology plays an important role in development. Digital technology in communication and universal access of mobile connectivity have created a significant opportunity for women to think about update their life style within a flexible space and time. Towards the end of 20th century, the information and communication sectors have been booming with some digital media platforms and accordingly the market have been loaded with Smartphone which is an important tool for communication, business, entertainment and education. Easy access of mobile data for connectivity along with digital media platforms gives opportunities to women to use their skill and resource for economic independence. The case study explores the role of social media as a catalyst for women’s economic independence in Assam, India. In recent times, it has been noticed that women in Assam have increasingly engaged with social media platforms such as Facebook, YouTube, X, Instagram and WhatsApp to generate income, access markets and build entrepreneurial venture. To do the entire study the researcher adopt mixed methods approaches which combines in-depth interview with 10 women across rural and urban Assam, survey of 50 participants and analysis of social media usage data. The study examines how digital platforms help women to overcome geographical barriers, traditional gender roles and limited access to employment. Finding reveal that social media contributes to economic independence providing platforms where women can sell their resources and skills. Expanding market through online network can enter global market through E-commerce and facilitating skill development.

Can social media platforms be used to foster improved environmental behaviour in recreational fisheries?

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.

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 use of social media (SM) platforms in higher education has seen significant growth since the global pandemic disrupted traditional learning methods and forced stakeholders to explore alternatives. This trend is expected to continue with the rise of newer digital learning platforms, tools, programs and pedagogies, particularly within the context of the Fourth Industrial Revolution and the evolution towards Education 4.0 and Education 5.0. Despite a significant level of research into SM’s role in informal learning, there has been limited exploration of its integration into pedagogical practices, particularly regarding assessment challenges and innovations in university settings. Additionally, there is scarce research on mobile applications in academics, hindering the effective integration of SM platforms into curricula. This study addresses these gaps by examining stakeholder perceptions of using SM for educational purposes in a university setting in Qatar. Following a comprehensive and comparative literature review and analysis, a thematic analysis of interviews with 20 stakeholders revealed that SM platforms go beyond communication, serving as tools to enhance professional identity, reputation, research profiles, knowledge dissemination, and social practices, but they lack methodologies to integrate effective learning, either independently or in formal learning/teaching/assessment settings. This study proposes a framework to optimize the use of SM platforms, with a particular focus on sustainability, for lifelong learning and knowledge-based societies leading to more sustainable and prosperous economies. The findings encourage the reconsideration of digital assessment and the expansion pedagogical practices in alignment with modern social learning through an expansive array of SM tools and platforms.