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Description
Teacher attrition and the migration between schools and districts can have a negative impact on quality of education and teacher performance. Novice teachers leave the profession because they are overwhelmed by the workload and responsibilities of the job. In a previous action research cycle, I found that novice teachers' perceptions of isolation and lack of opportunities to share experiences had a negative effect on teacher perceptions of efficacy. This action research project examines the effect of leveraging social media and professional learning communities to provide opportunities for a group of novice teachers to share experiences and seek advice. By addressing the challenges that novice teachers face and providing solutions for common problems, it is the hope of this researcher that highly effective teachers will remain in the classroom. The results of the study indicate that the combined use of Twitter and YouTube in collaboration with professional learning communities will improve teacher perceptions of efficacy. Teachers who participated in the social media based professional learning communities are also more likely to remain in the classroom.
ContributorsBostick, Bradley Alan (Author) / Zambo, Ronald (Thesis advisor) / Heck, Thomas (Committee member) / Isai, Shelley (Committee member) / Arizona State University (Publisher)
Created2013
Description
The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.
ContributorsRavikumar, Srijith (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Social media offers a powerful platform for the independent digital content producer community to develop, disperse, and maintain their brands. In terms of information systems research, the broad majority of the work has not examined hedonic consumption on Social Media Sites (SMS). The focus has mostly been on the organizational perspectives and utilitarian gains from these services. Unlike through traditional commerce channels, including e-commerce retailers, consumption enhancing hedonic utility is experienced differently in the context of a social media site; consequently, the dynamic of the decision-making process shifts when it is made in a social context. Previous research assumed a limited influence of a small, immediate group of peers. But the rules change when the network of peers expands exponentially. The assertion is that, while there are individual differences in the level of susceptibility to influence coming from others, these are not the most important pieces of the analysis--unlike research centered completely on influence. Rather, the context of the consumption can play an important role in the way social influence factors affect consumer behavior on Social Media Sites. Over the course of three studies, this dissertation will examine factors that influence consumer decision-making and the brand personalities created and interpreted in these SMS. Study one examines the role of different types of peer influence on consumer decision-making on Facebook. Study two observes the impact of different types of producer message posts with the different types of influence on decision-making on Twitter. Study three will conclude this work with an exploratory empirical investigation of actual twitter postings of a set of musicians. These studies contribute to the body of IS literature by evaluating the specific behavioral changes related to consumption in the context of digital social media: (a) the power of social influencers in contrast to personal preferences on SMS, (b) the effect on consumers of producer message types and content on SMS at both the profile level and the individual message level.
ContributorsSopha, Matthew (Author) / Santanam, Raghu T (Thesis advisor) / Goul, Kenneth M (Committee member) / Gu, Bin (Committee member) / Arizona State University (Publisher)
Created2013
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 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.
- 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.
ContributorsHu, Yuheng (Author) / Kambhampati, Subbarao (Thesis advisor) / Horvitz, Eric (Committee member) / Krumm, John (Committee member) / Liu, Huan (Committee member) / Sundaram, Hari (Committee member) / Arizona State University (Publisher)
Created2014
Description
Sports communication is a vibrant, blossoming research area within the communication discipline. One of the more fruitful directions in sports communication research pertains to social media. Social media has embedded itself in the sports world in a very short period of time. As a result, there is a need for instructional resources that prepare students to understand the nuances and power that social media possess. This research provides the foundation for a case study textbook centered on social media and sports communication. Specifically, four cases dealing with: (a) athletes using social media to encourage input from fans; (b) sports organizations using social media as an agenda-setting tool; (c) negative parasocial interaction expressed to athletes via social media; and (d) athletes using social media to enact image repair are presented. These cases demonstrate that social media is a valuable conduit between athletes and fans that enables athletes and sports organizations to cultivate fan identity and maintain control over public information. The cases also demonstrate that fan behavior via social media can quickly turn problematic, requiring that athletes and sports organizations respond appropriately, yet strategically. The research concludes by offering implications for future social media and sports communication research.
ContributorsSanderson, Jimmy (Author) / Kassing, Jeffrey W. (Thesis advisor) / Ramirez Jr, Artemio (Committee member) / Meân, Lindsey J. (Committee member) / Arizona State University (Publisher)
Created2012
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