This sandwich thesis of works published from 2010 – 2017 takes up the discursive articulation of “social media” as a mobilizing concept in relation to a variety of other concerns: authorship and popular fiction, writing and publishing, archives and everyday life, celebrity and the opaque morality of media promotion. The project addresses social networking platforms (primarily Twitter and Facebook) and those who serve and critique their interests (authors, readers, academics, “everyday people,” national archives, celebrities and filmmakers), often focusing on the “meta” of the media they take as their focus: extratexts, reviews and interviews, tweets about books and books about tweets, critical reception, etc. It considers “social media” as an idea or, more accurately, a system or constellation of ideas, a discourse or discourses beyond the mere technological. It examines the authority and impact of these discourses—not the use or usefulness of social media, but the ways these media are taken up, avoided, buttressed and manipulated in the most casual to the most politically contingent venues. In order to better comprehend and articulate the ideas, investments and ideological frameworks grounding social media discourse, this collective work traces and critically assesses the comparisons we make in an effort to render these media familiar and readable; the genealogies we construct in an effort to contextualize them and make their meanings legible; the stories we tell and the venues in which we tell them, to harness their creation and existence for other means, to authorize and deauthorize, to empower and disavow. By examining writing on and about social media, this work offers an alternative, context-specific approach to new media scholarship that, in its examination of things said and unsaid, will help inform our contemporary understanding of social media and, by extension, our social media experience. / Thesis / Doctor of Philosophy (PhD) / This sandwich thesis of works published from 2010 – 2017 considers how we talk and write about social media in relation to a variety of other concerns: authorship and popular fiction, writing and publishing, archives and everyday life, celebrity and the opaque morality of media promotion. The project addresses social networking platforms (primarily Twitter and Facebook) and those who serve and critique their interests (authors, readers, academics, “everyday people,” national archives, celebrities and filmmakers), often focusing on the “meta” of the media they take as their focus: “extratexts,” reviews and interviews, tweets about books and books about tweets, critical reception, etc. By examining writing on and about social media, this work offers an alternative, context-specific approach to new media scholarship that, in its examination of things said and unsaid, will help inform our contemporary understanding of social media and, by extension, our social media experience.

This research set out to explore how individuals used Twitter during the Gatlinburg, Tennessee wildfire event of November 2016. More specifically, how and what did people from different geolocations share via Twitter during the crisis event and what gratifications were sought by their use of Twitter. A content analysis was completed on a stratified sample of tweets separated by geographic location. Based on prior uses and gratifications research, tweet and retweet content was coded as informational, social, and/or distractive. Findings from this research showed that individuals tweeted and retweeted at a fairly even rate despite their geographical location and that while information seeking and sharing was an important factor for users, social connectivity was the most important gratification to users during this crisis. / Master of Arts

Online social networks are experiencing an explosive growth in recent years in both the number of users and the amount of information shared. The users join these social networks to connect with each other, share, find content and disseminate information by sending short text messages in near realtime. As a result of the growth of social networks, the users are often experiencing information overload since they interact with many other users and read ever increasing content volume. Thus, finding the "matching" users and content is one of the key challenges for social networks sites. Recommendation systems have been proposed to help users cope with information overload by predicting the items that a user may be interested in. The users' preferences are shaped by personal interests. At the same time, users are affected by their surroundings, as determined by their geographically located communities. Accordingly, our approach takes into account both personal interests and local communities. We first propose a new dynamic recommendation system model that provides better customized content to the user. That is, the model provides the user with the most important tweets according to his individual interests. We then analyze how changes in the surrounding environment can affect the user's experience. Specifically, we study how changes in the geographical community preferences can affect the individual user's interests. These community preferences are generally reflected in the localized trending topics. Consequently, we present TrendFusion, an innovative model that analyzes the trends propagation, predicts the localized diffusion of trends in social networks and recommends the most interesting trends to the user. Our performance evaluation demonstrate the effectiveness of the proposed recommendation system and shows that it improves the precision and recall of identifying important tweets by up to 36% and 80%, respectively. Results also show that TrendFusion accurately predicts places in which a trend will appear, with 98% recall and 80% precision. / Ph. D.

While many people enjoy hiking as a weekend activity, to many outdoor enthusiasts there is a hiking culture with which they feel affiliated. However, the way that these cultures interact with each other is still unclear. Exploring these different cultures and understanding how they relate to each other can help in engaging stakeholders of the trail. This is an important step toward finding ways to encourage environmentally friendly outdoor recreation practices and developing hiker-approved (and environmentally conscious) technologies to use on the trail. We explored these cultures by analyzing an extensive collection of tweets (over 1.5 million). We used topic modeling to identify the topics described by the communities of Triple Crown trails. We labeled training data for a classifier that identifies tweets relating to depreciative behaviors on the trail. Then, we compared the distribution of tweets across various depreciative trail behaviors to those of corresponding blog posts in order to see how tweets reflected cultures in comparison with blog posts. To harness metadata beyond the text of the tweets, we experimented with visualization techniques. We combined those efforts with ethnographic studies of hikers and conservancy organizations to produce this exploration of trail cultures. In this thesis, we show that through the use of natural language processing, we can identify cultural differences between trail communities. We identify the most significantly discussed forms of trail depreciation, which is helpful to conservation organizations so that they can more appropriately share which Leave No Trace practices hikers should place extra effort into practicing. / Master of Science / In a memoir of her hike on the Pacific Crest Trail, Wild, Cheryl Strayed said to a reporter in an amused tone, “I’m not a hobo, I’m a long-distance hiker”. While many people enjoy hiking as a weekend activity, to many outdoor enthusiasts there is a hiking culture with which they feel affiliated. There are cultures of trail conservation, and cultures of trail depreciation. There are cultures of long-distance hiking, and there are cultures of day hiking and weekend warrior hiking. There are also cultures across different hiking trails—where the hikers of one trail have different sets of values and behaviors than for another trail. However, the way that these cultures interact with each other is still unclear. Exploring these different cultures and understanding how they relate to each other can help in engaging stakeholders of the trail. This is an important step toward finding ways to encourage environmentally friendly outdoor recreation practices and developing hiker-approved (and environmentally conscious) technologies to use on the trail. We decided to explore these cultures by analyzing an extensive collection of tweets (over 1.5 million). We combined those expoorts with ethnographic style studies of conservancy organizations and avid hikers to produce this exploration of trail cultures.

Most of the research done on microblogging services, such as Twitter, has focused on how the individual communicates with their community at a micro and macro level; less research has been done on how the community affects the individual. We present in this thesis some ideas about this phenomenon. We do this by collecting data of Twitter users at a conference. We collected 21,150 tweets from approximately 400 users during a five week period and additionally collected survey data from a small subset of the tweeters. By observing users of Twitter, before, during, after a specific event we discovered a pattern in postings. Specifically, we found that tweets increased the week of the conference and that by the end of the conference the network was strong. These findings lead us to conclude that collocation of communities, like conferences, has a substantial effect on online microblogging behaviors. / Master of Science

With the growing need for a sociological understanding of behavior on social media platforms, there is a desire to know how marginalized groups engage with these technologies. This study asks whether queer people on Twitter utilize the platform to create a counterpublic - a group of strangers linked by a shared oppositional discourse to the dominant public discourse. To answer this, I compare the interaction patterns and thematic content of queer tweets with a previously identified Twitter counterpublic, Black Twitter, and dominant public, liberal and conservative Twitter. To locate queer Twitter content, I developed a process that intakes a starting term speculating where a community may be and finds hashtags used most by accounts that recently tweeted with the starting term. Using the starting term "#lgbtq," I discovered that #gay and #lgbt were the most used during the observation period. I also conducted this process to find the most used hashtags for the liberal Twitter community (#voteblue and #redwave), the conservative Twitter community (#trump and #maga), and the Black Twitter community (#blackpanther and #kyrie). By analyzing levels of engagement using a negative binomial regression, I find that queer tweets are significantly more likely to receive replies than those from the other communities. Using hierarchical cluster analysis and structured topic modeling, I conduct a content analysis that reveals that a large portion (70%) of queer tweets relate to pornographic content. Through posting intimate content, these tweets express oppositional sexualities excluded in dominant publics. I claim that queer people create a counterpublic on Twitter because tweets using queer hashtags show a higher level of commentary-based communication than the other Twitter communities and develop unique thematic content distinct and oppositional to the dominant public. Future research should build upon these findings to discover other avenues of queer online community outside of this narrow band of online communication. / Master of Science / In my thesis, I ask whether queer people on Twitter create an online community. To answer this, I compare how queer people interact and what they discuss with previously identified Twitter communities. To locate queer Twitter content, I developed a process that intakes a starting term to speculate where a community may be and finds hashtags used most by accounts who recently tweeted with the starting term. Using the starting term "#lgbtq" to estimate queer Twitter content, I discovered that #gay and #lgbt were the most used during the observation period. I also conducted this process to find the most used hashtags for the liberal Twitter community (#voteblue and #redwave), the conservative Twitter community (#trump and #maga), and the Black Twitter community (#blackpanther and #kyrie). By analyzing the levels of engagement, I found that queer tweets are more likely to receive replies than those from the other communities. My content analysis revealed that a large portion (70%) of the queer tweets included pornographic content. Through posting intimate content, these tweets express sexualities that dominant communities exclude. I claim that queer people create a community on Twitter because tweets using queer hashtags show a higher level of commentary-based communication than the other Twitter communities and develop unique discussion content. However, my findings are limited to a narrow band of online communication. Future research should build upon my research to discover other avenues of queer online community.

Yes / The governments, nowadays, throughout the world are increasingly becoming dependent on public opinion regarding the framing and implementation of certain policies for the welfare of the general public. The role of social media is vital to this emerging trend. Traditionally, lack of public participation in various policy making decision used to be a major cause of concern particularly when formulating and evaluating such policies. However, the exponential rise in usage of social media platforms by general public has given the government a wider insight to overcome this long pending dilemma. Cloud-based e-governance is currently being realized due to IT infrastructure availability along with mindset changes of government advisors towards realizing the various policies in a best possible manner. This paper presents a pragmatic approach that combines the capabilities of both cloud computing and social media analytics towards efficient monitoring and controlling of governmental policies through public involvement. The proposed system has provided us some encouraging results, when tested for Goods and Services Tax (GST) implementation by Indian government and established that it can be successfully implemented for efficient policy making and implementation.

Yes / Social media provides a significant avenue for stakeholder engagement which is crucial to ensure loyalty and satisfaction of stakeholders who possess valuable resources that can influence the business outcomes. Container lines – imperative members of global supply chains and facilitators of international trade – utilize social media to engage their stakeholders due to environmental and commercial complexity of their business. However, not all social media posts generate the same amount of stakeholder engagement. This study aims to identify and examine the social media post characteristics that lead to higher stakeholder engagement in the container shipping market. The study applies Chi-Squared Automatic Interaction Detection method to categorize social media posts based on their engagement levels. The analysis is conducted on the tweets of four global container lines which are posted between 1 September 2018 and 31 January 2019. The results demonstrate that social media posts of container lines have varying effects on engagement level. We found that fluency of tweets, tangibility of company resources in the tweet, vividness level, content type, existence of a link, and existence of a call-to-action significantly influence the container lines’ stakeholder engagement rate. This study is the first that finds out social media post classes based on the interaction between their characteristics and engagement rates by employing a decision tree methodology. The results are expected to help container lines in their social media management and stakeholder engagement policies.

Yes / People start posting tweets containing texts, images, and videos as soon as a disaster hits an area. The analysis of these disaster-related tweet texts, images, and videos can help humanitarian response organizations in better decision-making and prioritizing their tasks. Finding the informative contents which can help in decision making out of the massive volume of Twitter content is a difficult task and require a system to filter out the informative contents. In this paper, we present a multi-modal approach to identify disaster-related informative content from the Twitter streams using text and images together. Our approach is based on long-short-term-memory (LSTM) and VGG-16 networks that show significant improvement in the performance, as evident from the validation result on seven different disaster-related datasets. The range of F1-score varied from 0.74 to 0.93 when tweet texts and images used together, whereas, in the case of only tweet text, it varies from 0.61 to 0.92. From this result, it is evident that the proposed multi-modal system is performing significantly well in identifying disaster-related informative social media contents.

Yes / Social media is a platform to express one’s view in real time. This real time nature of social media makes it an attractive tool for disaster management, as both victims and officials can put their problems and solutions at the same place in real time. We investigate the Twitter post in a flood related disaster and propose an algorithm to identify victims asking for help. The developed system takes tweets as inputs and categorizes them into high or low priority tweets. User location of high priority tweets with no location information is predicted based on historical locations of the users using the Markov model. The system is working well, with its classification accuracy of 81%, and location prediction accuracy of 87%. The present system can be extended for use in other natural disaster situations, such as earthquake, tsunami, etc., as well as man-made disasters such as riots, terrorist attacks etc. The present system is first of its kind, aimed at helping victims during disasters based on their tweets.