In this thesis the structure and usage of narrative within a social media platform isexplored, with Twitter as the chosen social media network. Narrative and storytellingwithin a social media sphere such as Twitter primarily focuses on bringing a certaincharacterization and voice to a user account with story and narrative generally coming insecond place. By studying and comparing real-life cases of storytelling on twitter I havecome to the conclusion that a narrative structure that combines a good character with awell-executed characterization is the ideal way of using Twitter as a platform forstorytelling.

No description available.

Master of Science / Department of Computer Science / Major Professor Not Listed / There are many approaches to detect if an app is malware or benign, for example, using static or dynamic analysis. Static analysis can be used to look for APIs that are indicative of malware. Alternatively, emulating the app’s behavior using dynamic analysis can also help in detecting malware. Each type of approach has advantages and disadvantages. To complement existing approaches, in this report, I studied the use of Twitter data to identify malware. The dataset that I used consists of a large set of Android apps made available by AndroZoo. For each app, AndroZoo provides information on vt detection, which records number of anti-virus programs in VirusTotal that label the app as malware. As an additional source of information about apps, I crawled a large set of tweets and analyzed them to identify patterns of malware and benign apps in Twitter. Tweets were crawled based on keywords related to Google Play Store app links. A Google Play Store app link contains the corresponding app’s ID, which makes it easy to link tweets to apps. Certain fields of the tweets were analyzed by comparing patterns in malware versus benign apps, with the goal of identifying fields that are indicative of malware behavior. The classification label from AndroZoo was considered as ground truth.

Master of Science / Department of Agricultural Economics / Glynn T. Tonsor / Over the past decade, social media has risen from an emerging novelty to the normative form of expression for many Americans. As these platforms have risen in popularity, researchers have recognized the potential for capturing information users are self-reporting about their beliefs and preferences. Simultaneously, social media corporations have become privy to the value of this information being freely shared by consumers and have safeguarded much of their historical data to monetize the data. Faced with both an enticing new source of data, but a steep price to obtain it, researchers must evaluate the potential gains that can be extracted from the often difficult to analyze data. This study explored the acquisition of social media, namely Twitter, data and the potential uses in the field of agriculture economics. A contract was secured with Sysomos, a social media analytics firm, in July of 2017 to collect raw Twitter data over the proceeding thirteen months. Changes in frequency of tweets and sentiment scoring of tweets were used to attempt to explain election results from November 2017 proposed legislations pertaining to marijuana and minimum wage as well as to explain and predict changes in the stock prices of selected publicly traded firms in the food producing sector. Twitter frequency changes were then compared to changes in traditional print media articles in an effort to determine the exchangeability of the two media sources when used to track events pertaining to animal health. Results of this study suggested that Twitter data possess little power to explain the studied election results, but creation of a strong model was difficult due to the limited number of months of data available. Changes in the frequency of tweets were not found to be a strong indicator of changes in the stock market on the average day, but were shown to explain potentially highly valued information to investors on days with large changes in price. Twitter and traditional print media were shown to be unique sources of data when exploring the topic of animal health events.

This research project examines the link between social media and its effect on stock exchanges and movement of stocks. The study uses Twitter as its primary social media platform and focuses on its effect on the Johannesburg Stock Exchange. The study examines various forms of social media and micro-blogging sites in its attempt to provide a thorough understanding of the role of social media within the market. In line with its exploration of social media, the study analyses User-Generated Content, Sentiment Analysis and the impact of Word-of-Mouth. A brief explanation of Algorithmic Trading, the Efficient Market Hypothesis, and the Adaptive Market Hypothesis is also provided. The information used to show the relationship between Twitter and the JSE was extracted using a quantitative survey answered by registered traders on the JSE. The survey aimed to ascertain the level of information pertaining to stock movement posted to the platform by these traders, and how these traders used that same information to make trading decisions. The results of the study show that Twitter and other micro-blogging sites have a level of determination in stock exchanges. This study shows that traders make some use of online information to inform their trading decisions on the Stock Market. The validity of this online information stems from the fact that traders place trust in other people and other users’ experience, as proven by Word of Mouth. The findings of this study were contrary to the researchers’ expectation that Twitter was widely used as an informant for trade decisions. What is deduced from the available findings is that while Twitter and other social media platforms do to some extent provide information for traders on the JSE in making trade decisions, it is not a wide-spread basis for movement of shares. / Dissertation (MBA)--University of Pretoria, 2013. / zkgibs2014 / Gordon Institute of Business Science (GIBS) / MBA / Unrestricted

Broadcasts and timelines are the primary mechanism of information exchange in online social platforms today. Services like Facebook, Twitter and Instagram have enabled ordinary people to reach large audiences spanning cultures and countries, while their massive popularity has created increasingly competitive marketplaces of attention. Timing broadcasts to capture the attention of such geographically diverse audiences has sparked interest from many startups and social marketing gurus. However, formal study is lacking on both the timing and frequency problems. In this thesis, we introduce, motivate and solve the broadcast scheduling problem of specifying the timing and frequency of publishing content to maximise the attention received. We validate and quantify three interacting behavioural phenomena to parametrise social platform users: information overload, bursty circadian rhythms and monotony aversion, which is defined here for the first time. Our analysis of the influence of monotony refutes the common assumption that posts on social network timelines are consumed piecemeal independently. Instead, we reveal that posts are consumed in chunks, which has important consequences for any future work considering human behaviour over social network timelines. Our quantification of monotony aversion is also novel, and has applications to problems in various domains such as recommender list diversification, user satiation and variety-seeking consumer behaviour. Having studied the underlying behavioural phenomena, we link schedules, timelines, attention and behaviour by formalising a timeline information exchange process. Our formulation gives rise to a natural objective function that quantifies the expected collective attention an arrangement of posts on a timeline will receive. We apply this formulation as a case-study on real-data from Twitter, where we estimate behavioural parameters, calculate the attention potential for different scheduling strategies and, using the method of marginal allocation, discover a new scheduling strategy that outperforms popular scheduling heuristics while producing fewer posts.

The aim of this dissertation is to examine selective exposure theory on Twitter among student users at Kuwait University, and to revisit selective exposure theory’s assumptions in the social media era. Two studies for this dissertation have been conducted among a total of 1391 participants to examine the selective exposure theory among student Twitter users. In both studies, the researcher conducted an online experiment by developing simulated Twitter interface pages and a simulated news app to study selective exposure theory among Kuwait University students. The first study aimed to examine whether the students at Kuwait University tend to be exposed to politicians in Kuwait’s parliament who share the same political ideologies. The second study aimed to examine to what extent student users selectively expose themselves to specific content on Twitter, or more specifically to their like-minded group, and what drives them to do so. Moreover, the effect of Twitter’s social endorsement features on users' news selection has been examined. Each study sample has been drawn from different classes of students at Kuwait University. This study also looked at the role of incidental exposure as a means of encouraging cross-ideological exposure. One noticeable trend in the two experiments conducted for this dissertation is that partisan selective exposure was clear among students participating in both studies, but at different levels. Also, data showed that there was no clear role for the social endorsements on Twitter among students in this experiment to reduce selectivity. Moreover, a person's political leaning is more likely to surpass the impact of the social endorsements when users are browsing Twitter on a daily basis. Results showed that students in the second study read tweets from accounts they did not follow in real life and they asserted that they experienced that on a high basis. Implications of these two online experiment studies are discussed.

As new technology has emerged in the digital era, the public can now choose from a variety of new media from which to get weather information. Weather applications (apps) and social media have emerged as some of the popular new media. This study sought to understand the extent to which these new media are used, how weather apps are perceived, how the news media used Twitter during Hurricane Irma, and how the public engaged with the news media’s tweets. A survey and dataset of tweets were used to evaluate the research questions and hypotheses of this research. The study found that most survey participants used digital sources for weather information, even in severe weather. The weather app was the most used source of all age brackets, though held a stronger majority amongst younger demographics. Numerous relationships were found between weather app usage and gender, smartphone brand and reliance, time of app usage, and app usage frequency. Participants who downloaded a non-standard weather app onto their phone had higher self-perceived weather knowledge and interest. Weather app users perceived their app to be accurate and sometimes inconsistent, which were both found to be correlated to trust. Perceived app accuracy was also moderately correlated with other aspects of the field of meteorology. Respondents indicated that they accounted for uncertainty in a forecast with time and for regional variability of weather when determining if the forecast verified. However, both conclusions will require further research. The final study of this dissertation found that content, frequency, and engagement with news media tweets during Irma fluctuated over the storm’s duration and a relationship was found between content and engagement. Smaller television markets showed less coverage and overall change in coverage and engagement compared to larger markets. Finally, a meteorologist’s tweeting of personal content prior to the storm was found to be weakly correlated with the number of retweets received during the storm.

This study aims to establish if a relationship between the investor sentiment generated from social media posts, such as Tweets, and the return on securities exists. If a relationship exists, one would be able to obtain an informational advantage from public information and outperform the market on a risk-adjusted basis. This would give the “outsider” information processed the predictive power of insider information, hence the title of the paper. The study makes use of Bloomberg's social activity data, which through natural language processing, allows for investor sentiment to be obtained by analysing a combination of Twitter and Stock Twits posts. This paper makes use of a three-prong approach, firstly examining if investor sentiment is a predictor of next-day returns. Next, an event study methodology is used to examine the optimal holding period, which can further be expanded to test market efficiency. Lastly, this paper considers the asymmetric risk aversion as outlined by Kahneman and Tversky (1979). Results show that there is little to no correlation between sentiment and next day returns. There is evidence for a multi-day holding period being optimal but statistically insignificant and there is no evidence found for asymmetric risk aversion.

Because of the speed and convenience of Twitter, it has become one of the most widely utilized platforms for breaking news and is often used to raise awareness of current social issues, political happenings, and social injustices. As more women use Twitter and other social media to embrace the feminist label online, an array of criticism has come to surface. A new movement, #WomenAgainstFeminism, has become popular with Twitter users who reject feminism ideals and the feminism label. Research has been done examining the presence of online feminism, “hashtag feminism,” and online activism in general. Currently, there is no research analyzing the online reaction to feminism, #WomenAgainstFeminism. The purpose of this study is to analyze tweets containing #WomenAgainstFeminism to identify the salient reasons for rejecting feminism, stereotypes associated with feminism, and characteristics associated with feminists. This study broadens the current literature that analyzes attitudes towards feminism, stereotypes of feminists, and feminist identification. This study also adds to the growing body of literature that appreciates the impact Twitter and other social media networks have on members of society and social movements. This study differs from previous research because it focuses on the common stereotypes and characteristics associated with feminism that are prevalent in a social media campaign created to refute feminism.