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- Creators: Barrett, The Honors College
- Creators: Kavazanjian, Edward
Description
President Donald Trump announced his candidacy in June 2015, and the America immediately knew that he was an unorthodox candidate. Early on in his campaign, Trump isolated groups of people and treated them as enemies, but none so consistently as the news media. What began as criticism of "fake news," turned into calling the news media "the opposition party." However, media professionals agree that when the Trump administration called the news media the "enemy of the American people" \u2014 a line had been crossed. In the last two years Trump has denied simple fact and credible journalism countless times. His avid use of social media allows his messages to reach millions of people in moments - which had the potential to be a positive thing. However, Twitter is often where Trump turns to dispute the media, science, fact or anything else that "opposes" him. If Americans cannot believe the news media, cannot believe science, and cannot believe established fact, what can they believe? Allowing one man, in this case, Trump, to become the beacon of truth is dangerous and destructive to democracy. The news media must do their best to recapture the trust and faith of the American people by producing good, honest journalism. Seasoned journalism professionals say that his attacks on the media are likely a facade, just another way to appeal to his base, but that those attacks have the potential to wreak havoc in American society. Regardless of Trump's intentions, the toxicity between him and news media could have consequences that reach far beyond his presidency.
ContributorsPenningroth, Kendra Rene (Author) / Crane, Steve (Thesis director) / Downie, Leonard (Committee member) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / School of Criminology and Criminal Justice (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
This project analyzes the tweets from the 2016 US Presidential Candidates' personal Twitter accounts. The goal is to define distinct patterns and differences between candidates and parties use of social media as a platform. The data spans the period of September 2015 to March 2016, which was during the primary races for the Republicans and Democrats. The overall purpose of this project is to contribute to finding new ways of driving value from social media, in particular Twitter.
ContributorsMortimer, Schuyler Kenneth (Author) / Simon, Alan (Thesis director) / Mousavi, Seyedreza (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Partial differential equation (PDE) models are widely used for modeling processes in the physical sciences, economics, and sociology, but are otherwise new to the realm of social media. They allow researchers to construct a single spatiotemporal mathematical model to predict, in the case of this study, the level of information saturation at particular points in space at specific times. Utilizing data from the popular social network Twitter, this study presents a preliminary work looking into the effects of aggregating spatial data on such a PDE model. In other literature, the source of analytical and statistical bias that results from arbitrary spatial aggregation is known as the modifiable areal unit problem (MAUP). We use a previously-studied dataset from the 2011 Egyptian revolution for simulation, and group data points using several distance metrics based on geographical location and geo-cultural similarity. This paper will attempt to show that a PDE model, necessarily dependent upon aggregating data, is subject to significant bias when said data are arbitrarily organized and grouped for simulation. We look primarily into the zoning problem, which amounts to maintaining a fixed number of regions located in different areas across the globe, but make note of the scale problem, an inherent issue in PDE modeling that results from aggregating data points into increasingly larger regions. From looking at specific values from each simulation, this study shows that such a model is not free from the MAUP and that consideration of how data are aggregated needs to be made for future studies. In addition, it also suggests that geo-political and geo-cultural spatial metrics generate better diffusive patterns for tweet propagation than do simple geographical proximity metrics.
ContributorsRaymond, Ross Edward Scott (Author) / Kwon, Kyounghee Hazel (Thesis director) / Gruber, Diane (Committee member) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Social media is used by people every day to discuss the nuances of their lives. Major League Baseball (MLB) is a popular sport in the United States, and as such has generated a great deal of activity on Twitter. As fantasy baseball continues to grow in popularity, so does the research into better algorithms for picking players. Most of the research done in this area focuses on improving the prediction of a player's individual performance. However, the crowd-sourcing power afforded by social media may enable more informed predictions about players' performances. Players are chosen by popularity and personal preferences by most amateur gamblers. While some of these trends (particularly the long-term ones) are captured by ranking systems, this research was focused on predicting the daily spikes in popularity (and therefore price or draft order) by comparing the number of mentions that the player received on Twitter compared to their previous mentions. In doing so, it was demonstrated that improved fantasy baseball predictions can be made through leveraging social media data.
ContributorsRuskin, Lewis John (Author) / Liu, Huan (Thesis director) / Montgomery, Douglas (Committee member) / Morstatter, Fred (Committee member) / Industrial, Systems (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Bots tamper with social media networks by artificially inflating the popularity of certain topics. In this paper, we define what a bot is, we detail different motivations for bots, we describe previous work in bot detection and observation, and then we perform bot detection of our own. For our bot detection, we are interested in bots on Twitter that tweet Arabic extremist-like phrases. A testing dataset is collected using the honeypot method, and five different heuristics are measured for their effectiveness in detecting bots. The model underperformed, but we have laid the ground-work for a vastly untapped focus on bot detection: extremist ideal diffusion through bots.
ContributorsKarlsrud, Mark C. (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
The goal of this thesis research was to figure out if there were tangible differences between the way men and women speak on Twitter, a micro blogging social media site, and to see if there ways to apply it to strategizing marketing campaigns. AntConc, a free concordance software by Lawrence Anthony, was used to help organize and analyze a corpus created from the Tweets that were collected form the public accounts of twelve different popular public figures. These individuals were chosen based on their profession or the industry that they are associated with, as well as their general popularity. The research focused on three main industries or professions that can be viewed as ‘gendered;’ which were ‘Modeling,’ ‘Fashion Publications,’ and ‘Sports.’ The data was then analyzed across five different main categories which included, ‘Additional Media,’ ‘Adjective Usage,’ ‘How are they talking?,’ ‘Who are they talking about?, and ‘What are they talking about?’ The primary data, along with secondary research was used to see if they words and language use of men and women aligned with stereotypical patterns or if there were patterns that were unique and overlooked.
What was found was that although gender did play a large part in the way men and women spoke, there were more similarities when comparing individuals of the same industry or profession, than there were if they were simply analyzed just based on gender. Additionally, there were many factors that made it difficult to say whether these were qualified patterns or simply tendencies. More research into this would be able to help marketing companies and individuals, better target the audience they want for social media campaigns, by taking into account the importance in contemporary differences in language use by men and women. However, this research would have to be done on data from sites like Twitter to provide an accurate depiction of the way men and women, on these very unique mediums, speak.
What was found was that although gender did play a large part in the way men and women spoke, there were more similarities when comparing individuals of the same industry or profession, than there were if they were simply analyzed just based on gender. Additionally, there were many factors that made it difficult to say whether these were qualified patterns or simply tendencies. More research into this would be able to help marketing companies and individuals, better target the audience they want for social media campaigns, by taking into account the importance in contemporary differences in language use by men and women. However, this research would have to be done on data from sites like Twitter to provide an accurate depiction of the way men and women, on these very unique mediums, speak.
ContributorsChan, Kayla Rose (Author) / Adams, Karen (Thesis director) / Shinabarger, Amy D. (Committee member) / Barrett, The Honors College (Contributor) / Department of Marketing (Contributor) / Department of English (Contributor)
Created2015-05
Description
This study examined the brand personality types and social media practices of six college athletic Twitter accounts. Specifically, this study investigated whether certain brand personalities corresponded with specific social media practices on Twitter. The author conducted a content analysis of each school's tweets to measure brand personality and scraped data in order to collect social media practice information. Results suggest that brand personality and social media practices are distinct. Extraversion was the most common personality type among all schools. In addition, schools that tweeted less frequently than others exhibited more brand personality and used more visual media.
ContributorsDave, Simran Sangita (Author) / Gilpin, Dawn (Thesis director) / Reed, Sada (Committee member) / Pucci, Jessica (Committee member) / School of Life Sciences (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Social media is changing the way journalists operate; their use of Twitter is potentially representational of that change. Because of Twitter, journalists can connect to stories, sources, and audiences in ways they never could before. Because this is an evolving practice, role models can be difficult to find, which presents a problem for journalism students. In broadcast journalism, the challenge is even more pronounced when it comes to finding women exemplars for female students; female students are more likely to relate to female role models.This study, using in-depth interviews and textual analysis, examines how Twitter is being used by four prominent journalists in one competitive market. The Twitter feeds of four female TV news anchors in Phoenix, Arizona, the 12th largest broadcast market in the United States, are explored in terms of content and practice. The results show that they used Twitter daily and for more than just tweeting out the day's news, suggesting that Twitter has become a standard journalistic tool and a practice worth emulating.
ContributorsMolina, Tara Lea (Author) / Lodato, Mark (Thesis director) / Thornton, Leslie (Committee member) / Barrett, The Honors College (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor)
Created2014-05
Description
In 2012, Chick-fil-A president Dan Cathy's "came out" about his anti-same sex marriage views, launching an enormous negative backlash across social media networks. To counteract this, former governor Mike Huckabee called on his Facebook fans to support the company on "Chick-fil-A Appreciation Day," both on Facebook and in person. The project examines both the backlash and Appreciation Day on social media networks. Posts on the Appreciation Day Facebook event page and similar posts on Twitter were first broken down in the framework of supportive and oppositional posts and then analyzed in further contexts. Comments on official Chick-fil-A Facebook statuses were then examined in a similar fashion. The research concludes that a strong support system both online and offline were necessary for Chick-fil-A to recover from its backlash. The controversy that ensued is ultimately a case study in the growing influence of Facebook as a tool for small-scale activism.
ContributorsKuiland, Zachary Rico (Author) / Cheong, Pauline (Thesis director) / Szeli, Eva (Committee member) / Lim, Merlyna (Committee member) / Barrett, The Honors College (Contributor) / School of Criminology and Criminal Justice (Contributor) / Department of Psychology (Contributor)
Created2013-05
Description
Twitter has become a very popular social media site that is used daily by many people and organizations. This paper will focus on the financial aspect of Twitter, as a process will be shown to be able to mine data about specific companies' stock prices. This was done by writing a program to grab tweets about the stocks of the thirty companies in the Dow Jones.
ContributorsLarson, Grant Elliott (Author) / Davulcu, Hasan (Thesis director) / Ye, Jieping (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-05