No description available.

No description available.

Law enforcement agencies across the United States are under pressure to renew their commitment to strengthening community relationships while continuing to promote public safety and reduce crime. This renewed commitment has been catapulted by a series of events that have served to tarnish the image and reputation of law enforcement. In response, there has been a reinvigorated national discussion of how to enhance the image of police as an organization that has positive community relationships. The International Association of Chiefs of Police (2015) and The President’s Task Force on 21st Century Policing (2015) focus on the importance of building police-community relationships in the “Post-Ferguson” era of policing. Toward this end, the Task Force sees enormous potential of social media to bolster the police’s image and reputation. Research on police uses of social media, however, is very limited. This dissertation therefore explores the image-making efforts of twelve police organizations across the United States. By integrating organizational image construction from communication theory with the study of policing, this research examines the organizational identities and intended images that agencies are trying to project based on perspectives from interviews with those responsible for agency communications. It then compares these identities and intended images to the content produced on Twitter and Facebook over a twelve-month period using content analysis. This research found that across the diverse agencies examined here, there is a clear and consistent commitment to enhancing the community-oriented image of police. Respondents emphasized the value of humanizing police work and lending transparency to their actions and decisions as organizations. Content on agency websites equally revealed this commitment to positive community relationships. However, the content analysis of media feeds told a more nuanced story. Although each of the agencies examined disseminate community-oriented messaging, the traditional police mission of investigating crimes and solving criminal cases remains strong. Overall, social media content reveals efforts by police to delicately balance their crime-fighting and community-oriented identities. This balance varies by agency size, jurisdiction, and platform suggesting that the pressures governing image-making activities must be further examined in local context. This research seeks to demonstrate the value of applying an organizational image construction approach to police-community relations in our age of social media. This cross-disciplinary approach provides a framework for policy-makers and practitioners to assess whether their social media content aligns with their intended organizational identities and maximizes the ability to maintain a positive reputation. / Criminal Justice

Yes / This special issue on feminisms and social media is published at a unique point in time, namely when social media platforms are routinely utilised for communication from the mundane to the extraordinary, to offer support and solidarity, and to blame and victimise. Collectively, social media are online technologies that provide the ability for community building and interaction (Boyd & Ellison, 2007), allowing people to interact, share, create and consume online content (Lyons, McCreanor, Goodwin, & Moewaka Barnes, 2017). They include such platforms as Twitter, Facebook, YouTube, Tinder, and Snapchat among others.

With the current media world we are living in, it is now more relevant than ever to know how much we are affected by the media we consume the most in our daily lives, social media. This isn’t an unknown phenomena and politicians all over the world knows this, and they have therefore put more effort to spread their information more on different platforms. How much are we affected by it and is it related to gullibility? A survey research was made on university students from all over the world. A total of 86 participants were used. A correlation between social media usage and political influence was found, but none with gullibility. Studies with other factors than gullibility could be of interest to use as a variable for future studies.

Nowadays, knowledge discovery on social media is attracting growing interest. Social media has become more than a communication tool, effectively functioning as a social sensor for our society. This dissertation focuses on the development of methods for social media-based spatiotemporal event detection and forecasting for a variety of event topics and assumptions. Five methods are proposed, namely dynamic query expansion for event detection, a generative framework for event forecasting, multi-task learning for spatiotemporal event forecasting, multi-source spatiotemporal event forecasting, and deep learning based epidemic modeling for forecasting influenza outbreaks. For the first of these methods, existing solutions for spatiotemporal event detection are mostly supervised and lack the flexibility to handle the dynamic keywords used in social media. The contributions of this work are: (1) Develop an unsupervised framework; (2) Design a novel dynamic query expansion method; and (3) Propose an innovative local modularity spatial scan algorithm. For the second of these methods, traditional solutions are unable to capture the spatiotemporal context, model mixed-type observations, or utilize prior geographical knowledge. The contributions of this work include: (1) Propose a novel generative model for spatial event forecasting; (2) Design an effective algorithm for model parameter inference; and (3) Develop a new sequence likelihood calculation method. For the third method, traditional solutions cannot deal with spatial heterogeneity or handle the dynamics of social media data effectively. This work's contributions include: (1) Formulate a multi-task learning framework for event forecasting; (2) simultaneously model static and dynamic terms; and (3) Develop efficient parameter optimization algorithms. For the fourth method, traditional multi-source solutions typically fail to consider the geographical hierarchy or cope with incomplete data blocks among different sources. The contributions here are: (1) Design a framework for event forecasting based on hierarchical multi-source indicators; (2) Propose a robust model for geo-hierarchical feature selection; and (3) Develop an efficient algorithm for model parameter optimization. For the last method, existing work on epidemic modeling either cannot ensure timeliness, or cannot characterize the underlying epidemic propagation mechanisms. The contributions of this work include: (1) Propose a novel integrated framework for computational epidemiology and social media mining; (2) Develop a semi-supervised multilayer perceptron for mining epidemic features; and (3) Design an online training algorithm. / Ph. D.

The rise of big data, especially social media data (e.g., Twitter, Facebook, Youtube), gives new opportunities to the understanding of human behavior. Consequently, novel computing methods for mining patterns in social media data are therefore desired. Through applying these approaches, it has become possible to aggregate public available data to capture triggers underlying events, detect on-going trends, and forecast future happenings. This thesis focuses on developing methods for social media analysis. Specifically, five directions are proposed here: 1) semi-supervised detection for targeted-domain events, 2) topical interaction study among multiple datasets, 3) discriminative learning about the identifications for common and distinctive topics, 4) epidemics modeling for flu forecasting with simulation via signals from social media data, 5) storyline generation for massive unorganized documents. / Ph. D.

The rapid growth of visual microblogging platforms, such as Instagram, has created new opportunities for brands to communicate with stakeholders. As these platforms evolve, brands have had to adapt in order to use the available social media platforms to gain visibility in the millennial audience. Recently brands have turned to online 'celebrities' known as a social media influencer (SMI) to distribute information and influence consumers' product perceptions. This specifically has become a common tactic in communication and marketing efforts with the fashion and beauty industry. Ample research is available on the effects of celebrity endorsements but currently there is a gap in research pertaining to the consumer's perspective towards SMIs and SMIs effects on consumers. The online experiment completed in this thesis addressed how promotion of a product by a SMI affects perceptions of consumers on Instagram by measuring social comparison and self-congruity. This is accomplished by comparing participant's product perception to promotional posts on Instagram by a SMI, brand, and unbranded retail source. A three-condition experiment (SMI, Brand, Control) compared effects of product perception, social comparison, and self-congruity. A questionnaire consisting of 48 questions pertaining to SMI, self-congruity, social comparison, and product perception was completed by 151 participants. Significant relationships were found between the source of the promotional post (SMI, Brand, Control) and product perception. There was also a correlation between self-congruity and social comparison towards the SMI as well as product perception. Results suggest that the post source influences product perception. Results also indicate consumers' perception of the SMI effects product perception. These results provide practical implications for communication practioners who utilize social media. The rapid growth of visual microblogging platforms such as Instagram, is creating new opportunities for organizations to communicate with stakeholders. Brands have used social media platforms in order to gain visibility in the college age audience. Currently there is a gap in research pertaining to SMI and their effects on consumers. This online experiment will address how promotion of a product by an SMI affects perceptions of consumers on Instagram through social comparison and self-congruity theory by comparing responses to a product promoted by an SMI to the same product promoted by the promoted by the brand and to an unbranded retail source. A questionnaire consisting 34 of questions pertaining to SMI, self-congruity, and social comparison will be asked to 180-240 participants. The participants will be randomly assigned one of nine Instagram posts to accomplish stimulus sampling across the three conditions: three from SMI, three from brands, and three from an unbranded retail source / Master of Arts

Businesses have recognized that customers provide value to the firm beyond transactions, and leveraging this value through relationships in social media is a new area of interest for both academics and practitioners. Recent research has investigated how businesses can best manage their online presence on platforms not fully under their control, such as Facebook, YouTube, Instagram, TripAdvisor, and Yelp, among others. This dissertation extends the literature of customer engagement in social media through four contributions. First, we propose a framework that foregrounds the textual artifacts involved in online communication. Second, we develop a novel method for discovering the elements of successful Business to Customer (B2C) messages in online communities. Third, we propose a method, validated through experimentation, for finding critical product feedback in Customer to Customer (C2C) communications. Finally, we demonstrate that a set of novel numerical features can enhance the discovery of product defect mentions in C2C communications. We conclude by proposing a research agenda suggested by the framework that will further enhance our understanding of the complex customer interactions that characterize business in the era of social media. / Ph. D.

Yes / Twitter journal clubs are a relatively new adaptation of an established continuing professional development (CPD) activity within healthcare. The medical radiation science (MRS) journal club 'MedRadJClub' (MRJC) was founded in March 2015 by a group of academics, researchers and clinicians as an international forum for the discussion of peer-reviewed papers. To investigate the reach and impact of MRJC, a five-year analysis was conducted. Tweetchat data (number of participants, tweets and impressions) for the first five years of MRJC were extracted and chat topics organised into themes. Fifth anniversary MRJC chat tweets were analysed and examples of academic and professional outputs were collated. A total of 59 chats have been held over five years with a mean of 41 participants and 483,000 impressions per hour-long synchronous chat. Ten different tweetchat themes were identified, with student engagement/preceptorship the most popular. Eight posters or oral presentations at conferences, one social media workshop and four papers have been produced. Qualitative analysis revealed five core themes relating to the perceived benefits of participation in MRJC: (1) CPD and research impact, (2) professional growth and influencing practice, (3) interdisciplinary learning and inclusion, (4) networking and social support and (5) globalisation. MRJC is a unique, multi-professional, global community with consistent engagement. It is beneficial for both CPD, research engagement, dissemination and socialisation within the MRS community.