This thesis examines two organizational strategies employed for influencing American environmental policy with considerations to the "power elite" and "pluralist" models of policy control. Using a data set comprised of 379 organizations derived from US congressional hearings on climate change policy between 1976 and 2006, I find that industrial corporations conceal the public footprint of their involvement by financially encouraging "independent" research centers to provide favorable testimony. Meanwhile, nonprofit organizations are more likely to be co-represented by shared experts, a resource that is strategically shared for political gain. These findings provide some circumscribed support for both the power elite and pluralist models of organizational influence: the organization of power elites has a disproportionate amount of resources in a system that provides an arena for competing values and goals. Implications for understanding the organizational strategies towards congressional testimony as well as directions for future research are discussed based on these findings.

Web surveys in higher education are particularly important for evaluating the quality of academic teaching and learning. Traditionally, mainly quantitative data is used for quality assessment. Increasingly, questions are being raised about the impact of attitudes of individuals involved. Therefore, especially the analysis of open-ended text responses in web surveys offers the potential for impact evaluation. Despite the fact that qualitative text mining, sentiment analysis, and network analytics are being introduced in other research areas, these instruments are still slowly gaining access to evaluation research. On the one hand, there is a lack of methodological expertise to deal with large numbers of text responses (e.g. via semantic analysis, linguistically supported coding, etc.). On the other hand, deficiencies in interdisciplinary expertise are identified in order to be able to contextualize the results. The contribution contributes to the field of impact evaluation and reveals methodological implications for the development of text mining, sentiment analysis, and network analytics in evaluation processes.

Although they clearly exist, affinities among individuals are not all easily identified. Yet, they offer unique opportunities to discover new social networks, strengthen ties among individuals, and provide recommendations. We propose the idea of Implicit Affinity Networks (IANs) to build, visualize, and track affinities among groups of individuals. IANs are simple, interactive graphical representations that users may navigate to uncover interesting patterns. This thesis describes a system supporting the construction of IANs and evaluates it in the context of family history and online communities.

The Covid-19 pandemic presented unprecedented challenges, with global lockdowns impacting individuals on a profound scale. Many took to social media to cope with feelings of anxiety and isolation. Lockdown conditions and social media carry with them particular challenges, triggers and temptations for those with eating disorders, namely in the form of online communities promoting eating disorders and disordered behaviors as a legitimate lifestyle choice. This study examines pro-eating disorder Twitter communities before, during and after the initial Covid-19 lockdown (Mid-March to May 2020) to examine the influence of confinement on leadership dynamics and content trends. Utilizing data obtained through Twitter’s Academic API, I constructed monthly retweet network time-slices spanning from November 2019 to September 2020. Through social network analysis and analyzing turnover rates of top users, the evolution of influential users was assessed to test whether the circumstances created by Covid-19 restrictions would disrupt the established leadership paradigm or the period would maintain stable leadership based on expectations proposed by the literature of preferential attachment in scale-free networks. Contrary to expectations, influential users exhibited high turnover throughout the period and the network showed no tendency towards preferential attachment or any scale-free behavior in degree distributions. The high rate of leader turnover further increased in May and a higher proportion of new users achieved the highest number of in-degree ties into the latter months, but this hint at a cohort shift did not align with covid lockdown as predicted, instead occurring at the end of lockdown and continuing until the end of the studied period. Ultimately, users’ mostly fleeting popularity was largely based on the current content interests of the group rather than the individual user’s network position. The increase in activity predicted to co-occur with covid restrictions did not materialize until the summer months, therefore cannot be definitively linked to lockdown. The fluctuations in topic popularity detected in the topic model suggest a possible seasonal component to the rhythms of this community that requires further research. This exclusive longitudinal analysis of retweet networks as they were affected by covid-19 lockdown conditions challenges previous research on influence in social networks and online communities with findings of more dynamic leadership. Understanding the influence dynamics of this community can inform efforts to combat the spread of potentially harmful content and provide valuable insights for eating disorder specialists navigating the influences that may affecting their patients.

This dissertation examines emergent forms of digitally enabled boundary-spanning by considering the within- and cross-field interactions developed by LGBTQIA* organisations on social media. Within scholarship on collective actors characterised by strong collective identities, LGBTQIA* collective action fields have been conventionally found to be rather fragmented and polarised, as well as isolated from other fields. Nonetheless, recent studies have shown evidence of the emergence of cooperative and solidarity efforts by LGBTQIA* actors, suggesting that such LGBTQIA* organisations may actively engage in crossing categorical boundaries and overcoming differences in the attempt to achieve social change. Within this framework, information and communication technologies (ICTs) may provide LGBTQIA* communities with spaces to converge, share experiences, and articulate politicised identities also through the connection with other collective actors. Despite these findings, we currently lack a systematic understanding of the extent to which LGBTQIA* collective actors span field boundaries through the development of digitally enabled interactions, of how such ties evolve over time, and on the circumstances that may favour or inhibit their emergence and duration. Furthermore, the role of ICTs in supporting the emergence of boundary-spanning processes has so far been rather understudied. This dissertation addresses these concerns by conducting a mixed-method comparative research on LGBTQIA* actors based in Madrid and Milan. In particular, it focuses on different types of interactions (mentioning, sharing, and promoting collective action events) developed by such organisations on their Facebook public pages during the 2011-2020 decade. To examine the role of both cultural patterns (collective identities and framing strategies) and structural circumstances (political opportunities and threats), this study combines network and text data, analysed through social network and frame analysis. The findings provided by this research show that Spanish and Italian LGBTQIA* organisations increasingly crossed categorical boundaries through social media between 2011 and 2020, thus suggesting that ICTs do play a role in sustaining boundary-spanning processes. Moreover, they find that collective framing and networking are inextricably entangled, and hence contribute to shedding light on both symbolic and behavioural dimensions of digitally enabled boundary-spanning. To conclude, they show that actors embedded in different socio-political contexts engage in networking and framing, thus emphasising the role of contextual opportunities and threats in moderating the nexus between ICTs and boundary-spanning, as well as between framing and networking. This dissertation contributes to both social movement literature and gender studies. On the one hand, by shedding light on emergent forms of boundary-spanning processes enabled by ICTs, it not only contributes to examining the role of ICTs in empowering marginalised communities, but also further elaborates the entanglement between digital, hybrid, and on-the-ground collective actions. On the other hand, by systematically investigating an emerging phenomenon over time and across contexts, it contributes to generating knowledge on the circumstances encouraging collective actors to overcome differences and cooperate. Understanding this is of utmost importance, since cooperative relations provide collective actors with additional and diverse resources and experiences, as well as political legitimation, to resist processes of democratic erosion and achieve social change.

The influence of Information and Communication Technologies (ICTs) particularly internet technology has had a fundamental impact on the way government is administered, provides services and interacts with citizens. Currently, the use of social media is no longer limited to informal environments but is an increasingly important medium of communication between citizens and governments. The extensive and increasing use of social media will continue to generate huge amounts of user-generated content known as Big Social Data (BSD). The growing body of BSD presents innumerable opportunities as well as challenges for local government planning, management and delivery of public services to citizens. However, the governments have not yet utilised the potential of BSD for better understanding the public and gaining new insights from this new way of interactions. Some of the reasons are lacking in the mechanism and guidance to analyse this new format of data. Thus, the aim of this study is to evaluate how the body of BSD can be mined, analysed and applied in the context of local government in the UK. The objective is to develop a Big Social Data Analytics (BSDA) model that can be applied in the case of local government. Data generated from social media over a year were collected, collated and analysed using a range of social media analytics and network analysis tools and techniques. The final BSDA model was applied to a local council case to evaluate its impact in real practice. This study allows to better understand the methods of analysing the BSD in the public sector and extend the literature related to e-government, social media, and social network theory / Universiti Utara Malaysia

No description available.

No description available.

No description available.

No description available.