Matching Items (4)
Description
Live streaming has risen to significant popularity in the recent past and largely this live streaming is a feature of existing social networks like Facebook, Instagram, and Snapchat. However, there does exist at least one social network entirely devoted to live streaming, and specifically the live streaming of video games, Twitch. This social network is unique for a number of reasons, not least because of its hyper-focus on live content and this uniqueness has challenges for social media researchers.
Despite this uniqueness, almost no scientific work has been performed on this public social network. Thus, it is unclear what user interaction features present on other social networks exist on Twitch. Investigating the interactions between users and identifying which, if any, of the common user behaviors on social network exist on Twitch is an important step in understanding how Twitch fits in to the social media ecosystem. For example, there are users that have large followings on Twitch and amass a large number of viewers, but do those users exert influence over the behavior of other user the way that popular users on Twitter do?
This task, however, will not be trivial. The same hyper-focus on live content that makes Twitch unique in the social network space invalidates many of the traditional approaches to social network analysis. Thus, new algorithms and techniques must be developed in order to tap this data source. In this thesis, a novel algorithm for finding games whose releases have made a significant impact on the network is described as well as a novel algorithm for detecting and identifying influential players of games. In addition, the Twitch network is described in detail along with the data that was collected in order to power the two previously described algorithms.
Despite this uniqueness, almost no scientific work has been performed on this public social network. Thus, it is unclear what user interaction features present on other social networks exist on Twitch. Investigating the interactions between users and identifying which, if any, of the common user behaviors on social network exist on Twitch is an important step in understanding how Twitch fits in to the social media ecosystem. For example, there are users that have large followings on Twitch and amass a large number of viewers, but do those users exert influence over the behavior of other user the way that popular users on Twitter do?
This task, however, will not be trivial. The same hyper-focus on live content that makes Twitch unique in the social network space invalidates many of the traditional approaches to social network analysis. Thus, new algorithms and techniques must be developed in order to tap this data source. In this thesis, a novel algorithm for finding games whose releases have made a significant impact on the network is described as well as a novel algorithm for detecting and identifying influential players of games. In addition, the Twitch network is described in detail along with the data that was collected in order to power the two previously described algorithms.
ContributorsJones, Isaac (Author) / Liu, Huan (Thesis advisor) / Maciejewski, Ross (Committee member) / Shakarian, Paulo (Committee member) / Agarwal, Nitin (Committee member) / Arizona State University (Publisher)
Created2019
Description
The livestreaming platform Twitch allows users to engage with one another and with content creators, known as streamers, in real-time, creating a cyclical pattern in which viewers and streamers simultaneously influence one another and co-construct the livestreams. While this active engagement has resulted in numerous benefits, it has also led to a surge in toxic behavior – actions meant to disrupt the flow of the livestream and harm the streamer and viewers involved. Toxic behavior is often directed at individuals who do not conform to the norms of a space or community. Because Twitch evolved out of an interest in video game spectatorship, and video game culture is burdened by the gamer stereotype, which typecasts gamers as young, white, male, and cishet, Twitch users who do not fit this identity category (e.g., women; black, Indigenous and people of color [BIPOC]; queer people; etc.) are labeled as threats to the perceived homogeneity of video game (and Twitch) culture. This project examines toxic discourses surrounding three women Twitch streamers, considering how the streamers’ performances, community-building efforts, and methods of regulation impact the levels and types of toxicity in their livestreams. A critical technocultural discourse analysis of 30 hours of livestreaming data reveals diverse approaches to managing toxicity. While all three streamers expressed that they neither liked nor approved of toxic behavior, their methods of addressing it varied greatly, from active channel moderators and explicit rules to public acts of moderation. Furthermore, the manifestation of toxicity differed across the three streamers’ communities, signaling that the streamers’ strategies impact not only users’ willingness to engage in this behavior but also other viewers’ responses to this issue. Twitch’s positioning as a service provider, which places most of burden of regulating user behavior on streamers, further complicates this problem, as streamers are largely responsible for enforcing Twitch’s rules as well as their own, leading to disparate and conflicting social norms and enforcement patterns. This project underscores the need for Twitch and its streamers to create standardized methods of behavior regulation that are inclusive and hold users accountable for their behavior.
ContributorsRines, Olivia (Author) / Adams, Karen (Thesis advisor) / SturtzSreetharan, Cindi (Committee member) / Florini, Sarah (Committee member) / Arizona State University (Publisher)
Created2021
Description
Esports streaming has become an entertainment giant and promises to continue to grow in the coming years. Streaming platforms, such as Twitch and Youtube, have become a haven for community and competition, blending the two into a novel form of interaction that fuels business. This study will analyze how the streaming of esports has influenced business in the technological realm of electronic games and contributed to the field’s longevity. It questions how we, as a society, view community in the online world which itself has become a site for the expansion of how people interact. The study also incorporates the idea of business into the market of technological electronic game-based communities and how competition through esports has also been a fuel for both. Through literature analysis and data collection, the goal of this research would be to increase knowledge on the understanding of streaming esports and help predict what foundation it might take as a whole later on.
ContributorsLatimer, Travis D (Author) / Ingram-Waters, Mary (Thesis director) / Pierce, John (Committee member) / Computer Science and Engineering Program (Contributor) / WPC Graduate Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Abstract
Matrix Factorization techniques have been proven to be more effective in recommender systems than standard user based or item based methods. Using this knowledge, Funk SVD and SVD++ are compared by the accuracy of their predictions of Twitch streamer data.
Introduction
As watching video games is becoming more popular, those interested are becoming interested in Twitch.tv, an online platform for guests to watch streamers play video games and interact with them. A streamer is an person who broadcasts them-self playing a video game or some other thing for an audience (the guests of the website.) The site allows the guest to first select the game/category to view and then displays currently active streamers for the guest to select and watch. Twitch records the games that a streamer plays along with the amount of time that a streamer spends streaming that game. This is how the score is generated for a streamer’s game. These three terms form the streamer-game-score (user-item-rating) tuples that we use to train out models.
The our problem’s solution is similar to the purpose of the Netflix prize; however, as opposed to suggesting a user a movie, the goal is to suggest a user a game. We built a model to predict the score that a streamer will have for a game. The score field in our data is fundamentally different from a movie rating in Netflix because the way a user influences a game’s score is by actively streaming it, not by giving it an score based off opinion. The dataset being used it the Twitch.tv dataset provided by Isaac Jones [1]. Also, the only data used in training the models is in the form of the streamer-game-score (user-item-rating) tuples. It will be known if these data points with limited information will be able to give an accurate prediction of a streamer’s score for a game. SVD and SVD++ are the baseis of the models being trained and tested. Scikit’s Surprise library in Python3 is used for the implementation of the models.
Matrix Factorization techniques have been proven to be more effective in recommender systems than standard user based or item based methods. Using this knowledge, Funk SVD and SVD++ are compared by the accuracy of their predictions of Twitch streamer data.
Introduction
As watching video games is becoming more popular, those interested are becoming interested in Twitch.tv, an online platform for guests to watch streamers play video games and interact with them. A streamer is an person who broadcasts them-self playing a video game or some other thing for an audience (the guests of the website.) The site allows the guest to first select the game/category to view and then displays currently active streamers for the guest to select and watch. Twitch records the games that a streamer plays along with the amount of time that a streamer spends streaming that game. This is how the score is generated for a streamer’s game. These three terms form the streamer-game-score (user-item-rating) tuples that we use to train out models.
The our problem’s solution is similar to the purpose of the Netflix prize; however, as opposed to suggesting a user a movie, the goal is to suggest a user a game. We built a model to predict the score that a streamer will have for a game. The score field in our data is fundamentally different from a movie rating in Netflix because the way a user influences a game’s score is by actively streaming it, not by giving it an score based off opinion. The dataset being used it the Twitch.tv dataset provided by Isaac Jones [1]. Also, the only data used in training the models is in the form of the streamer-game-score (user-item-rating) tuples. It will be known if these data points with limited information will be able to give an accurate prediction of a streamer’s score for a game. SVD and SVD++ are the baseis of the models being trained and tested. Scikit’s Surprise library in Python3 is used for the implementation of the models.
ContributorsAitken, Connor Dalton (Author) / Liu, Huan (Thesis director) / Jones, Isaac (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05