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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.

In recent years we have seen an increase in the number of public transit service disruptions due to aging infrastructure, system failures and the regular need for maintenance. With the fleeting growth in the usage of these transit networks there has been an increase in the need for the timely detection of such disruptions. Any types of disruptions in these transit networks can lead to delays which can have major implications on the daily passengers. Most current disruption detection systems either do not operate in real-time or lack transit network coverage. The theme of this thesis was to leverage Twitter data to help in earlier detection of service disruptions. This work involves developing a pure Data Mining approach and a couple different approaches that use Graph Neural Networks to identify transit disruption related information in Tweets from a live Twitter stream related to the Washington Metropolitan Area Transit Authority (WMATA) metro system. After developing three different models, a Dynamic Query Expansion model, a Tweet-GCN and a Tweet-Level GCN to represent the data corpus we performed various experiments and benchmark evaluations against other existing baseline models, to justify the efficacy of our approaches. After seeing astounding results across both the Tweet-GCN and Tweet-Level GCN, with an average accuracy of approximately 87.3% and 89.9% we can conclude that not only are these two graph neural models superior for basic NLP text classification, but they also outperform other models in identifying transit disruptions. / Master of Science / Millions of people worldwide rely on public transit networks for their daily commutes and day to day movements. With the growth in the number of people using the service, there has been an increase in the number of daily passengers affected by service disruptions. This thesis and research involves proposing and developing three different approaches to help aid in the timely detection of these disruptions. In this work we have developed a pure data mining approach along with two deep learning models using neural networks and live data from Twitter to identify these disruptions. The data mining approach uses a set of dirsuption related input keywords to identify similar keywords within the live Twitter data. By collecting historical data we were able to create deep learning models that represent the vocabulary from the disruptions related Tweets in the form of a graph. A graph is a collection of data values where the data points are connected to one another based on their relationships. A longer chain of connection between two words defines a weak relationship, a shorter chain defines a stronger relationship. In our graph, words with similar contextual meanings are connected to each other over shorter distances, compared to words with different meanings. At the end we use a neural network as a classifier to scan this graph to learn the semantic relationships within our data. Afterwards, this learned information can be used to accurately classify the disruption related Tweets within a pool of random Tweets. Once all the proposed approaches have been developed, a benchmark evaluation is performed against other existing text classification techniques, to justify the effectiveness of the approaches. The final results indicate that the proposed graph based models achieved a higher accuracy, compared to the data mining model, and also outperformed all the other baseline models. Our Tweet-Level GCN had the highest accuracy of 89.9%.

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 / As visual social media platforms, such as Instagram, continue to rapidly grow in popularity, brands have been obligated to quickly learn how to utilize these platforms in order to reach their target audiences. Brands typically use social media platforms in order to gain visibility in the college aged audience, but new platforms require new strategies. A new popular tactic is utilizing an online “celebrity” known as a social media influencer (SMI) in order to distribute information and influence consumers’ perceptions. Using SMIs in communication and marketing campaigns has grown in popularity in industries such as beauty/fashion, home/family, health/fitness, travel/lifestyle, food/beverage, business/tech and entertainment. In beauty and fashion, the use of SMIs to reach the millennial audience has become a part of regular practice for companies such as H&M, Madewell, Gucci and others. There is ample research on the effects of celebrity endorsements but currently there is a gap in research pertaining to SMIs and their effects on consumers. This online experiment completed in this thesis addressed how promotion of a product by a SMI affects perceptions of consumers by measuring their social comparison and self-congruity. This is accomplished by comparing participant’s product perception to posts by SMI, brands, and unbranded retail sources that promoted a product on Instagram. 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 post and product perception. Correlations were found between self-congruity and social comparison towards the SMI, as well as product perception. Results suggest that where the source of the post influences product perception. Results also indicate that consumer’s perception of the SMI effects product perception. These results provide practical implications for communication and marketing professionals who are determining whether to use SMI and those who already use SMI.

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 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 dissertation provides comprehensive studies for social media data analysis. The goals of the dissertation include: event early detection, future event prediction, and event chain organization. Specifically, these goals are achieved through efforts in the following aspects: (1) semi-supervised and unsupervised methods are developed to collect early signals from social media data and detect on-going events; (2) graphical models are proposed to model the interaction and comparison among multiple datasets; (3) traditional computational methods are combined with new emerge social media data analysis for the purpose of fast epidemic prediction; (4) events in different time stamps are organized into event chains via novel probabilistic models. The effectiveness of our approaches is evaluated using various datasets, such as Twitter posts and news articles. Also, interesting case studies are provided to show models’ abilities in the real world exploration.