Matching Items (40)

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Modeling Fantasy Baseball Player Popularity Using Twitter Activity

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

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.

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Created

Date Created
  • 2017-05

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Analysis of the Aftereffects of Terror Attacks on Social Media

Description

Social media has become a direct and effective means of transmitting personal opinions into the cyberspace. The use of certain key-words and their connotations in tweets portray a meaning that

Social media has become a direct and effective means of transmitting personal opinions into the cyberspace. The use of certain key-words and their connotations in tweets portray a meaning that goes beyond the screen and affects behavior. During terror attacks or worldwide crises, people turn to social media as a means of managing their anxiety, a mechanism of Terror Management Theory (TMT). These opinions have distinct impacts on the emotions that people express both online and offline through both positive and negative sentiments. This paper focuses on using sentiment analysis on twitter hash-tags during five major terrorist attacks that created a significant response on social media, which collectively show the effects that 140-character tweets have on perceptions in social media. The purpose of analyzing the sentiments of tweets after terror attacks allows for the visualization of the effect of key-words and the possibility of manipulation by the use of emotional contagion. Through sentiment analysis, positive, negative and neutral emotions were portrayed in the tweets. The keywords detected also portray characteristics about terror attacks which would allow for future analysis and predictions in regards to propagating a specific emotion on social media during future crisis.

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Created

Date Created
  • 2016-12

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Analyzing the Brand Personalities and Social Media Practices of College Athletic Twitter Accounts

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

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.

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Created

Date Created
  • 2018-05

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President Donald Trump and the News Media

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

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.

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Created

Date Created
  • 2017-12

Predicting Bitcoin Price Trend using Sentiment Analysis

Description

In this paper I defend the argument that public reaction to news headlines correlates with the short-term price direction of Bitcoin. I collected a month's worth of Bitcoin data consisting

In this paper I defend the argument that public reaction to news headlines correlates with the short-term price direction of Bitcoin. I collected a month's worth of Bitcoin data consisting of news headlines, tweets, and the price of the cryptocurrency. I fed this data into a Long Short-Term Memory Neural Network and built a model that predicted Bitcoin price for a new timeframe. The model correctly predicted 75% of test set price trends on 3.25 hour time intervals. This is higher than the 53.57% accuracy tested with a Bitcoin price model without sentiment data. I concluded public reaction to Bitcoin news headlines has an effect on the short-term price direction of the cryptocurrency. Investors can use my model to help them in their decision-making process when making short-term Bitcoin investment decisions.

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Created

Date Created
  • 2020-05

We Should Talk: Consulting the Relationship Between Twitter and Sports Journalism

Description

This thesis documentary film takes a look at the dysfunctional but ongoing relationship between Twitter and sports journalism. The foundation of this relationship's dysfunction is what I have coined as

This thesis documentary film takes a look at the dysfunctional but ongoing relationship between Twitter and sports journalism. The foundation of this relationship's dysfunction is what I have coined as the Twitter Outrage Cycle. In this cycle a sports broadcasting personality comments on a matter while on-air. Next, the program's audience where the comments were spoken becomes offended by the statement. After that, the offended audience members express their outrage on social media, most namely Twitter. Finally the cycle culminates with the public outrage pressuring networks and its executives to either suspended or fire the individual that said the controversial statements. This cycle began to occur on a more consistent basis starting in 2012. It became such a regular occurrence that many on-air talent figures have noticed and taken precautionary measures to either avoid or confront the Outrage Cycles. This documentary uses the voice of seven figures within the sports media and online interaction forum. Notable using the voices of three notable individuals that currently have a prominent voice in sports journalism. As well as a neutral social media curator who clearly explains the psyche behind these outraged viewer's mindsets. Through these four main voices their ideals and opinions on the matter weave together, disagree with each other at times but ultimately help the viewer come to an understanding of why these Outrage Cycles occur and what needs to be done in order for them to cease. We Should Talk: The Relationship Between Twitter and Sports Journalism is a documentary film that looks to illustrate a seemingly minimal part of many people's lives that when taken into perspective many people look at in a very serious light.

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Created

Date Created
  • 2016-05

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A Brief look at the Effect of the Modifiable Areal Unit Problem on a Diffusive Logic Partial Differential Model for Information Diffusion in Social Media

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

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.

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Created

Date Created
  • 2016-05

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Twitter Use by Arizona Politicians: A Case Study and Analysis

Description

In the past ten years, social network services have expanded from a digital method in which the public connects with only their friends and families. Social network services have evolved

In the past ten years, social network services have expanded from a digital method in which the public connects with only their friends and families. Social network services have evolved to a highly-accessible, convenient, cost-effective tool to engage with communities beyond one's frequented social circle on a local, national, and global scale. Many politicians have adapted in order to use social network services to connect directly with their constituents. Politicians have begun to use their profiles on social network services as their own privately owned publicity channel, publishing raw "material" like political opinions or legal advocacy, appearances at events and media like photos, videos or links to maintain transparency and accessibility to their constituencies. The content analysis investigates the use of a social network service (Twitter) by five different Arizonan politicians from different municipal, state and federal offices over the period of six months. All posts on Twitter were recorded, evaluated, and categorized by content into one of seventeen different divisions: Constituent Connection, Correction, Culture, Economy, Education, Environment, Healthcare, Humanitarianism, International, Military, Operational, Personal, Political Activity, Reply to Constituent, Security, Social Issues or Sports. The date, category, content, media type and engagement (replies, retweets, and favorites) were also recorded. Understanding how political figures connect and engage with their constituencies contributes to understanding modern campaigning and modern government; politicians are now finding it imperative to have and maintain a social media presence in order to gain relevance, transparency and accessibility with their constituencies. This study examines how politicians are currently utilizing these micro-blogging sites.

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Created

Date Created
  • 2018-05

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Analysis of BoostOR: A Twitter Bot Detection Classification Algorithm

Description

The prevalence of bots, or automated accounts, on social media is a well-known problem. Some of the ways bots harm social media users include, but are not limited to, spreading

The prevalence of bots, or automated accounts, on social media is a well-known problem. Some of the ways bots harm social media users include, but are not limited to, spreading misinformation, influencing topic discussions, and dispersing harmful links. Bots have affected the field of disaster relief on social media as well. These bots cause problems such as preventing rescuers from determining credible calls for help, spreading fake news and other malicious content, and generating large amounts of content which burdens rescuers attempting to provide aid in the aftermath of disasters. To address these problems, this research seeks to detect bots participating in disaster event related discussions and increase the recall, or number of bots removed from the network, of Twitter bot detection methods. The removal of these bots will also prevent human users from accidentally interacting with these bot accounts and being manipulated by them. To accomplish this goal, an existing bot detection classification algorithm known as BoostOR was employed. BoostOR is an ensemble learning algorithm originally modeled to increase bot detection recall in a dataset and it has the possibility to solve the social media bot dilemma where there may be several different types of bots in the data. BoostOR was first introduced as an adjustment to existing ensemble classifiers to increase recall. However, after testing the BoostOR algorithm on unobserved datasets, results showed that BoostOR does not perform as expected. This study attempts to improve the BoostOR algorithm by comparing it with a baseline classification algorithm, AdaBoost, and then discussing the intentional differences between the two. Additionally, this study presents the main factors which contribute to the shortcomings of the BoostOR algorithm and proposes a solution to improve it. These recommendations should ensure that the BoostOR algorithm can be applied to new and unobserved datasets in the future.

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Created

Date Created
  • 2018-12

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Twitter Patterns in the Politics of Social Mobilization: #BlackLivesMatter Case Study

Description

The role of technology in shaping modern society has become increasingly important in the context of current democratic politics, especially when examined through the lens of social media. Twitter

The role of technology in shaping modern society has become increasingly important in the context of current democratic politics, especially when examined through the lens of social media. Twitter is a prominent social media platform used as a political medium, contributing to political movements such as #OccupyWallStreet, #MeToo, and #BlackLivesMatter. Using the #BlackLivesMatter movement as an illustrative case to establish patterns in Twitter usage, this thesis aims to answer the question “to what extent is Twitter an accurate representation of “real life” in terms of performative activism and user engagement?” The discussion of Twitter is contextualized by research on Twitter’s use in politics, both as a mobilizing force and potential to divide and mislead. Using intervals of time between 2014 – 2020, Twitter data containing #BlackLivesMatter is collected and analyzed. The discussion of findings centers around the role of performative activism in social mobilization on twitter. The analysis shows patterns in the data that indicates performative activism can skew the real picture of civic engagement, which can impact the way in which public opinion affects future public policy and mobilization.

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Created

Date Created
  • 2021-05