Matching Items (2)
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- All Subjects: Bias
- Creators: Carley, Kathleen M.
- Member of: ASU Electronic Theses and Dissertations
Description
Cultivation theory states that consuming television cultivates a social reality in the real world which aligns with the reality present in television. When the television show CSI was released, researchers studied a form of cultivation stemming from the show titled the "CSI Effect." One of the components of the CSI Effect is the tendency of those who watch CSI to be more likely to overestimate the presence of forensic evidence present in a trial and place more trust in such evidence. In recent years, several true crime documentaries that examined controversial cases have been released. In a similar vein of research conducted on CSI, the current study examines true crime documentaries and their possible impacts on viewers’ judgments and beliefs about the criminal justice system. In the current study, participants were provided with a mock case and asked about their perceptions of the case along with their viewership habits. While overall true crime documentary viewership did not influence judgments of evidence manipulation or perceptions of police, findings point to viewership of the targeted documentaries being associated with feelings of mistrust towards the criminal justice system overall, while the lesser-viewed documentaries correlated with judgments of strength and responsibility of the defendant in the case. One possible explanation is that individual characteristics may serve as the driving factor in how individuals choose what to watch when the popularity of the show is not as well-known.
ContributorsDoughty, Kathryn A (Author) / Schweitzer, Nicholas J. (Thesis advisor) / Neal, Tess (Committee member) / Salerno, Jessica (Committee member) / Arizona State University (Publisher)
Created2018
Description
Exabytes of data are created online every day. This deluge of data is no more apparent than it is on social media. Naturally, finding ways to leverage this unprecedented source of human information is an active area of research. Social media platforms have become laboratories for conducting experiments about people at scales thought unimaginable only a few years ago.
Researchers and practitioners use social media to extract actionable patterns such as where aid should be distributed in a crisis. However, the validity of these patterns relies on having a representative dataset. As this dissertation shows, the data collected from social media is seldom representative of the activity of the site itself, and less so of human activity. This means that the results of many studies are limited by the quality of data they collect.
The finding that social media data is biased inspires the main challenge addressed by this thesis. I introduce three sets of methodologies to correct for bias. First, I design methods to deal with data collection bias. I offer a methodology which can find bias within a social media dataset. This methodology works by comparing the collected data with other sources to find bias in a stream. The dissertation also outlines a data collection strategy which minimizes the amount of bias that will appear in a given dataset. It introduces a crawling strategy which mitigates the amount of bias in the resulting dataset. Second, I introduce a methodology to identify bots and shills within a social media dataset. This directly addresses the concern that the users of a social media site are not representative. Applying these methodologies allows the population under study on a social media site to better match that of the real world. Finally, the dissertation discusses perceptual biases, explains how they affect analysis, and introduces computational approaches to mitigate them.
The results of the dissertation allow for the discovery and removal of different levels of bias within a social media dataset. This has important implications for social media mining, namely that the behavioral patterns and insights extracted from social media will be more representative of the populations under study.
Researchers and practitioners use social media to extract actionable patterns such as where aid should be distributed in a crisis. However, the validity of these patterns relies on having a representative dataset. As this dissertation shows, the data collected from social media is seldom representative of the activity of the site itself, and less so of human activity. This means that the results of many studies are limited by the quality of data they collect.
The finding that social media data is biased inspires the main challenge addressed by this thesis. I introduce three sets of methodologies to correct for bias. First, I design methods to deal with data collection bias. I offer a methodology which can find bias within a social media dataset. This methodology works by comparing the collected data with other sources to find bias in a stream. The dissertation also outlines a data collection strategy which minimizes the amount of bias that will appear in a given dataset. It introduces a crawling strategy which mitigates the amount of bias in the resulting dataset. Second, I introduce a methodology to identify bots and shills within a social media dataset. This directly addresses the concern that the users of a social media site are not representative. Applying these methodologies allows the population under study on a social media site to better match that of the real world. Finally, the dissertation discusses perceptual biases, explains how they affect analysis, and introduces computational approaches to mitigate them.
The results of the dissertation allow for the discovery and removal of different levels of bias within a social media dataset. This has important implications for social media mining, namely that the behavioral patterns and insights extracted from social media will be more representative of the populations under study.
ContributorsMorstatter, Fred (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Maciejewski, Ross (Committee member) / Carley, Kathleen M. (Committee member) / Arizona State University (Publisher)
Created2017