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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 has generated a great deal of activity on Twitter. As

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 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 misinformation, influencing topic discussions, and dispersing harmful links. Bots have

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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Date Created
2018-12

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Social Media: A Case Study in Socially Connected Entrepreneurs

Description

The purpose of this study is to examine how social connectivity in a collaborative business environment translates to online social communication, namely to social media. Not a lot of academic research focuses in-depth on how startups and entrepreneurs within the

The purpose of this study is to examine how social connectivity in a collaborative business environment translates to online social communication, namely to social media. Not a lot of academic research focuses in-depth on how startups and entrepreneurs within the technology industry perceive social media, or how their work environment can influence the ways they see the role of these platforms. Gangplank was chosen as the subject of this case study based on the emphasis they as a coworking space put on connecting to others in order to accomplish mutual goals. Initial research showed that entrepreneurs using social media did so with a collaborative focus in mind. However, it was unclear if, by developing their businesses in a space devoted to fostering social relationships, entrepreneurs would be more likely to engage and interact with other users on social media platforms. Furthermore, it was unclear if their attitudes toward online and offline communication would be affected by spending time in a dedicated social workspace. In order to find how some entrepreneurs that started or worked closely in the beginning stages of a collaborative, connection-driven workspace used social media and see whether or not they used the platform to establish and build relationships and connect with others, three entrepreneurs from such a workspace were personally interviewed. In these interviews, each entrepreneur gave their personal feelings and opinions on the space itself, their view on the role of social media, and whether or not they connected their space to their social media use. The study also examined each entrepreneur's social media profile on one prominent social network to see how each was practically using the platform, and to analyze how each entrepreneur's use of the platform compared to his perceptions of social media as a whole. The study found that entrepreneurs who became established in a collaboration-oriented space definitely interacted frequently on social media. Each entrepreneur interviewed expressed the importance of working closely with others and forming valuable connections through both online and offline means. These entrepreneurs were established to have followed all the best practices of social media use outlined through research, and to have had a large number of personally engaging interactions and conversations on observed social media platforms.

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Created

Date Created
2015-12

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Analyzing the dynamics of communication in online social networks

Description

This thesis deals with the analysis of interpersonal communication dynamics in online social networks and social media. Our central hypothesis is that communication dynamics between individuals manifest themselves via three key aspects: the information that is the content of communication,

This thesis deals with the analysis of interpersonal communication dynamics in online social networks and social media. Our central hypothesis is that communication dynamics between individuals manifest themselves via three key aspects: the information that is the content of communication, the social engagement i.e. the sociological framework emergent of the communication process, and the channel i.e. the media via which communication takes place. Communication dynamics have been of interest to researchers from multi-faceted domains over the past several decades. However, today we are faced with several modern capabilities encompassing a host of social media websites. These sites feature variegated interactional affordances, ranging from blogging, micro-blogging, sharing media elements as well as a rich set of social actions such as tagging, voting, commenting and so on. Consequently, these communication tools have begun to redefine the ways in which we exchange information, our modes of social engagement, and mechanisms of how the media characteristics impact our interactional behavior. The outcomes of this research are manifold. We present our contributions in three parts, corresponding to the three key organizing ideas. First, we have observed that user context is key to characterizing communication between a pair of individuals. However interestingly, the probability of future communication seems to be more sensitive to the context compared to the delay, which appears to be rather habitual. Further, we observe that diffusion of social actions in a network can be indicative of future information cascades; that might be attributed to social influence or homophily depending on the nature of the social action. Second, we have observed that different modes of social engagement lead to evolution of groups that have considerable predictive capability in characterizing external-world temporal occurrences, such as stock market dynamics as well as collective political sentiments. Finally, characterization of communication on rich media sites have shown that conversations that are deemed "interesting" appear to have consequential impact on the properties of the social network they are associated with: in terms of degree of participation of the individuals in future conversations, thematic diffusion as well as emergent cohesiveness in activity among the concerned participants in the network. Based on all these outcomes, we believe that this research can make significant contribution into a better understanding of how we communicate online and how it is redefining our collective sociological behavior.

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Agent

Created

Date Created
2011

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Finding provenance data in social media

Description

A statement appearing in social media provides a very significant challenge for determining the provenance of the statement. Provenance describes the origin, custody, and ownership of something. Most statements appearing in social media are not published with corresponding provenance data.

A statement appearing in social media provides a very significant challenge for determining the provenance of the statement. Provenance describes the origin, custody, and ownership of something. Most statements appearing in social media are not published with corresponding provenance data. However, the same characteristics that make the social media environment challenging, including the massive amounts of data available, large numbers of users, and a highly dynamic environment, provide unique and untapped opportunities for solving the provenance problem for social media. Current approaches for tracking provenance data do not scale for online social media and consequently there is a gap in provenance methodologies and technologies providing exciting research opportunities. The guiding vision is the use of social media information itself to realize a useful amount of provenance data for information in social media. This departs from traditional approaches for data provenance which rely on a central store of provenance information. The contemporary online social media environment is an enormous and constantly updated "central store" that can be mined for provenance information that is not readily made available to the average social media user. This research introduces an approach and builds a foundation aimed at realizing a provenance data capability for social media users that is not accessible today.

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Agent

Created

Date Created
2011

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An information diffusion approach to detecting emotional contagion in online social networks

Description

Internet sites that support user-generated content, so-called Web 2.0, have become part of the fabric of everyday life in technologically advanced nations. Users collectively spend billions of hours consuming and creating content on social networking sites, weblogs (blogs), and various

Internet sites that support user-generated content, so-called Web 2.0, have become part of the fabric of everyday life in technologically advanced nations. Users collectively spend billions of hours consuming and creating content on social networking sites, weblogs (blogs), and various other types of sites in the United States and around the world. Given the fundamentally emotional nature of humans and the amount of emotional content that appears in Web 2.0 content, it is important to understand how such websites can affect the emotions of users. This work attempts to determine whether emotion spreads through an online social network (OSN). To this end, a method is devised that employs a model based on a general threshold diffusion model as a classifier to predict the propagation of emotion between users and their friends in an OSN by way of mood-labeled blog entries. The model generalizes existing information diffusion models in that the state machine representation of a node is generalized from being binary to having n-states in order to support n class labels necessary to model emotional contagion. In the absence of ground truth, the prediction accuracy of the model is benchmarked with a baseline method that predicts the majority label of a user's emotion label distribution. The model significantly outperforms the baseline method in terms of prediction accuracy. The experimental results make a strong case for the existence of emotional contagion in OSNs in spite of possible alternative arguments such confounding influence and homophily, since these alternatives are likely to have negligible effect in a large dataset or simply do not apply to the domain of human emotions. A hybrid manual/automated method to map mood-labeled blog entries to a set of emotion labels is also presented, which enables the application of the model to a large set (approximately 900K) of blog entries from LiveJournal.

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Created

Date Created
2011

Learning Users Visual Preferences: Building a Recommendation System for Instagram

Description

Social media users are inundated with information. Especially on Instagram--a social media service based on sharing photos--where for many users, missing important posts is a common issue. By creating a recommendation system which learns each user's preference and gives them

Social media users are inundated with information. Especially on Instagram--a social media service based on sharing photos--where for many users, missing important posts is a common issue. By creating a recommendation system which learns each user's preference and gives them a curated list of posts, the information overload issue can be mediated in order to enhance the user experience for Instagram users. This paper explores methods for creating such a recommendation system. The proposed method employs a learning model called ``Factorization Machines" which combines the advantages of linear models and latent factor models. In this work I derived features from Instagram post data, including the image, social data about the post, and information about the user who created the post. I also collect user-post interaction data describing which users ``liked" which posts, and this was used in models leveraging latent factors. The proposed model successfully improves the rate of interesting content seen by the user by anywhere from 2 to 12 times.

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Agent

Created

Date Created
2016-12

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Improving adolescent writing quality and motivation with Sparkfolio, a social media based writing tool

Description

Writing instruction poses both cognitive and affective challenges, particularly for adolescents. American teens not only fall short of national writing standards, but also tend to lack motivation for school writing, claiming it is too challenging and that they have nothing

Writing instruction poses both cognitive and affective challenges, particularly for adolescents. American teens not only fall short of national writing standards, but also tend to lack motivation for school writing, claiming it is too challenging and that they have nothing interesting to write about. Yet, teens enthusiastically immerse themselves in informal writing via text messaging, email, and social media, regularly sharing their thoughts and experiences with a real audience. While these activities are, in fact, writing, research indicates that teens instead view them as simply "communication" or "being social." Accordingly, the aim of this work was to infuse formal classroom writing with naturally engaging elements of informal social media writing to positively impact writing quality and the motivation to write, resulting in the development and implementation of Sparkfolio, an online prewriting tool that: a) addresses affective challenges by allowing students to choose personally relevant topics using their own social media data; and b) provides cognitive support with a planner that helps develop and organize ideas in preparation for writing a first draft. This tool was evaluated in a study involving 46 eleventh-grade English students writing three personal narratives each, and including three experimental conditions: a) using self-authored social media post data while planning with Sparkfolio; b) using only data from posts authored by one's friends while planning with Sparkfolio; and c) a control group that did not use Sparkfolio. The dependent variables were the change in writing motivation and the change in writing quality that occurred before and after the intervention. A scaled pre/posttest measured writing motivation, and the first and third narratives were used as writing quality pre/posttests. A usability scale, logged Sparkfolio data, and qualitative measures were also analyzed. Results indicated that participants who used Sparkfolio had statistically significantly higher gains in writing quality than the control group, validating Sparkfolio as effective. Additionally, while nonsignificant, results suggested that planning with self-authored data provided more writing quality and motivational benefits than data authored by others. This work provides initial empirical evidence that leveraging students' own social media data (securely) holds potential in fostering meaningful personalized learning.

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Agent

Created

Date Created
2014

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Understanding social media users via attributes and links

Description

With the rise of social media, hundreds of millions of people spend countless hours all over the globe on social media to connect, interact, share, and create user-generated data. This rich environment provides tremendous opportunities for many different players to

With the rise of social media, hundreds of millions of people spend countless hours all over the globe on social media to connect, interact, share, and create user-generated data. This rich environment provides tremendous opportunities for many different players to easily and effectively reach out to people, interact with them, influence them, or get their opinions. There are two pieces of information that attract most attention on social media sites, including user preferences and interactions. Businesses and organizations use this information to better understand and therefore provide customized services to social media users. This data can be used for different purposes such as, targeted advertisement, product recommendation, or even opinion mining. Social media sites use this information to better serve their users.

Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms.

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Agent

Created

Date Created
2014

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Event analytics on social media: challenges and solutions

Description

Social media platforms such as Twitter, Facebook, and blogs have emerged as valuable

- in fact, the de facto - virtual town halls for people to discover, report, share and

communicate with others about various types of events. These events range from

widely-known

Social media platforms such as Twitter, Facebook, and blogs have emerged as valuable

- in fact, the de facto - virtual town halls for people to discover, report, share and

communicate with others about various types of events. These events range from

widely-known events such as the U.S Presidential debate to smaller scale, local events

such as a local Halloween block party. During these events, we often witness a large

amount of commentary contributed by crowds on social media. This burst of social

media responses surges with the "second-screen" behavior and greatly enriches the

user experience when interacting with the event and people's awareness of an event.

Monitoring and analyzing this rich and continuous flow of user-generated content can

yield unprecedentedly valuable information about the event, since these responses

usually offer far more rich and powerful views about the event that mainstream news

simply could not achieve. Despite these benefits, social media also tends to be noisy,

chaotic, and overwhelming, posing challenges to users in seeking and distilling high

quality content from that noise.

In this dissertation, I explore ways to leverage social media as a source of information and analyze events based on their social media responses collectively. I develop, implement and evaluate EventRadar, an event analysis toolbox which is able to identify, enrich, and characterize events using the massive amounts of social media responses. EventRadar contains three automated, scalable tools to handle three core event analysis tasks: Event Characterization, Event Recognition, and Event Enrichment. More specifically, I develop ET-LDA, a Bayesian model and SocSent, a matrix factorization framework for handling the Event Characterization task, i.e., modeling characterizing an event in terms of its topics and its audience's response behavior (via ET-LDA), and the sentiments regarding its topics (via SocSent). I also develop DeMa, an unsupervised event detection algorithm for handling the Event Recognition task, i.e., detecting trending events from a stream of noisy social media posts. Last, I develop CrowdX, a spatial crowdsourcing system for handling the Event Enrichment task, i.e., gathering additional first hand information (e.g., photos) from the field to enrich the given event's context.

Enabled by EventRadar, it is more feasible to uncover patterns that have not been

explored previously and re-validating existing social theories with new evidence. As a

result, I am able to gain deep insights into how people respond to the event that they

are engaged in. The results reveal several key insights into people's various responding

behavior over the event's timeline such the topical context of people's tweets does not

always correlate with the timeline of the event. In addition, I also explore the factors

that affect a person's engagement with real-world events on Twitter and find that

people engage in an event because they are interested in the topics pertaining to

that event; and while engaging, their engagement is largely affected by their friends'

behavior.

Contributors

Agent

Created

Date Created
2014