Browsing by Subject "Twitter"

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Antisocialite: Presidential Tweets and the mobilization of Stop the Steal on January 6th
(2024-08-01) Dausch, Alexander
The January 6th, 2021, insurrection ushered in a new realm of possibilities around American presidential election results. Guided by Donald Trump’s tweets and rhetoric that day, the violence against the Capitol threatened the peaceful transition of power between presidential administrations. This study investigates how Trump’s tweets on January 6th created a worldview for his followers that made the violent acts against the Capitol that day reasonable and necessary for them to commit. While there have been extensive studies of the insurrection events, this paper presents a rhetorical frame analysis of Trump’s tweets and other rhetoric that day, while aligning the timing and content of his communications with the actions of those who committed the insurrection. This analysis and alignment bears examining because it provides insight into the real effects rhetoric and social media can have on the mobilization of social movements. The rhetorical frame analysis categorizes every Trump tweet from January 6th, demonstrating a progression from diagnostic, to prognostic, and finally to motivational framing in his rhetoric. The analysis also displays an alignment between Trump’s rhetorical frame progression and the violence on the Capitol. This particular type of frame analysis on this topic, coupled with the alignment shown with the insurrection actions fills a gap in this type of research. The implications of this research are that rhetoric has enough power to mobilize social movements into violent action, therefore making it necessary to analyze and understand the rhetorical tactics that were used to do so.
Corporate Sociality: An Analysis Of Twitter Post Directionality, Functionality, And Reciprocity Of Fortune 100 Companies
(2020-05) Erickson, Marisa
This study discusses the use of the social media platform Twitter by Fortune 100 companies. A random sample of the 2019 Twitter posts of 20 Fortune 100 companies over 30 days are collected. These posts are analyzed using a new theoretical model, titled The Three Faces of Corporate Social Media Use, as adapted from Grunig’s (1984) Four Models of Public Relations. According to Grunig (1992), the best model for companies to utilize is a two-way symmetrical model that promotes openness, trust, and understanding between organizations and their audiences. Contrasting that idea, this research found that companies most often use posts that are self-promotional, in that the posts carry messages that are promoting aspects of the company or are marketing a product or service.
Hate Speech Detection In Twitter: A Selectively Trained Ensemble Method
(2020-05) Houston, Jackson
This thesis tests classiﬁcation models from Natural Language Processing and Machine learning in the task of identifying hate speech. We tested on multiple annotated data sets (Davidson et al. 2017) of tweet data labeled as hate speech, oﬀensive speech, both, or neither. Hate speech has become an unavoidable topic in the current social media environment due to poorly monitored comment sections and news feeds. With that, studies showing the negative aﬀects that it brings to people’s well-being have also begun to surface (Gelber and McNamara 2015). Therefore, being able to identify hate speech accurately and precisely has grown in importance. Hate speech is often contextual, subjective, and a matter of opinion which makes creating an accurate model of such speech all the more diﬃcult. We have found that using an ensemble method of a classic Naive Bayes classiﬁer (Pedregosa et al. 2019c), Random Forest (Pedregosa et al. 2019b), K-Means (Pedregosa et al. 2019d), and Bernoulli (Pedregosa et al. 2019a) performed better than similar studies in precision, accuracy, recall, and f-score (Malmasi and Zampieri 2018). The ensemble performed better than using the strongest of the individual models, Random Forest, by a small but useful margin. We believe this to be due to the nuanced nature and context behind hate speech being more than one model can fully encompass. In addition to the ensemble strategy, training on data which was labeled as ‘clean’ (not hate speech or oﬀensive) or labeled ‘dirty’ (hate speech) with higher conﬁdence ratings increased the precision of our model by around 10% in some cases when compared to training on the complete data set including the tweets which have a blurred sentiment such as oﬀensive but not hate speech tweets. Having an accurate and precise model such as this will allow organizations to protect their users from such language to prevent the negative eﬀects of hate speech. Additionally, it will allow us to identify more hate speech tweets or statements to have more data to research in the future and ﬁnd deeper trends than simply the tweet text, such as replies, retweets, and user biographies.
Kite: A Scalable Microblogs Data Management System
(2017-06) Ahmed, Amr
Developers, researchers, and practitioners have been building a myriad of applications to analyze microblogs data, e.g., tweets, online reviews, and user comments. Examples of such applications include citizen journalism, events detection and analysis, geo-targeted advertising, medical research, and studying social influences in social sciences. Building such applications require data management infrastructure to deal with microblogs, including data digestion, indexing, and main-memory management. The lack of such infrastructure hinders the scalability and the widespread of such applications especially among users who are not computer scientists. This thesis proposes Kite; an end-to-end system that is able to manage microblogs data at a large scale. Using Kite, developers and practitioners can simply write SQL-like queries without worrying about the internal data management issues. Internally, Kite is equipped with scalable indexing and main-memory management techniques to support top-k temporal, spatial, keyword, and trending queries on both very recent data and historical data. Kite indexer supports scalable digestion and retrieval for incoming fast data in real time. Recent data are digested in efficient main-memory index structures. Kite in-memory index structure are able to scale up a single machine indexing capabilities to handle the overwhelming amount of data in real time. Meanwhile, Kite memory manager is monitoring the memory contents and smartly decides on which data is regularly moved to disk. This is accomplished through effective memory flushing policies that are designed for top-k query workloads, which are popular on microblogs data. Both in-memory and in-disk data are queried seamlessly through efficient retrieval techniques that are encapsulated in Kite query processor. The query processor exploits the top-k ranking function to early prune the search space and reduce the query latency significantly. Kite is open-sourced and available to the community to build on (http://kite.cs.umn.edu). Extensive experimentation on different Kite components show the efficiency and the effectiveness of the proposed techniques to manage microblogs data at scale.
Normalizing Twitter: Journalism Practice in an Emerging Communication Space
(Taylor and Francis, 2012) Lasorsa, Dominic L.; Lewis, Seth C.; Holton, Avery E.
This study examines how mainstream journalists who microblog negotiate their professional norms and practices in a new media format that directly challenges them. Through a content analysis of more than 22,000 of their tweets (postings) on the microblog platform Twitter, this study reveals that the journalists more freely express opinions, a common microblogging practice but one which contests the journalistic norm of objectivity (impartiality and nonpartisanship). To a lesser extent, the journalists also adopted two other norm-related microblogging features: providing accountability and transparency regarding how they conduct their work and sharing user-generated content with their followers. The journalists working for national newspapers, national television news divisions, and cable news networks were less inclined in their tweets than their counterparts working for less “elite” news outlets to relinquish their gatekeeping role by sharing their stage with other news gatherers and commentators, or to provide accountability and transparency by providing information about their jobs, engaging in discussions with other tweeters, writing about their personal lives, or linking to external websites.
Quantifying Political Leaning from Tweets, Retweets, and Retweeters
(IEEE Transactions on Knowledge and Data Engineering, 2016) Wong, Felix MFW; Tan, Chee Wei; Sen, Soumya; Chiang, Mung
The widespread use of online social networks (OSNs) to disseminate information and exchange opinions, by the general public, news media and political actors alike, has enabled new avenues of research in computational political science. In this paper, we study the problem of quantifying and inferring the political leaning of Twitter users. We formulate political leaning inference as a convex optimization problem that incorporates two ideas: (a) users are consistent in their actions of tweeting and retweeting about political issues, and (b) similar users tend to be retweeted by similar audience. Then for evaluation and a numerical study, we apply our inference technique to 119 million election-related tweets collected in seven months during the 2012 U.S. presidential election campaign. Our technique achieves 94% accuracy and high rank correlation as compared with manually created labels. By studying the political leaning of 1,000 frequently retweeted sources, 230,000 ordinary users who retweeted them, and the hashtags used by these sources, our numerical study sheds light on the political demographics of the Twitter population, and the temporal dynamics of political polarization as events unfold.
Towards a networked gatekeeping theory: Journalism, news diffusion, and democracy in a networked media environment
(2014-01) Ernste, Thomas John
This dissertation describes the development of a networked gatekeeping theory for the study of an increasingly internet-mediated news diffusion process. Prior gatekeeping research provides a rich theoretical and methodological framework for investigating and illuminating the process through which certain events and issues on an international, national, and local level become the mediated messages that reach the public. Towards a framework for reconceptualizing gatekeeping theory in which I incorporate principles of graph theory and social network analysis, I describe the development of a more participatory but still asymmetrically structured networked gatekeeping process that is forming according to the communication infrastructure afforded by the internet and its associated technologies. In particular, this dissertation focuses on the implications of these developments for both the practice of and research about news diffusion, journalism, internet-mediated communication, and democracy. In an empirical study of the Twitter-based news ecology of a large Midwestern metropolitan area, I conduct a case study using primarily social network analysis methods that uncovers insights about the patterns that emerge within this dynamic participatory news construction and diffusion process. The findings of this dissertation can be useful for media scholars, media practitioners, and for anyone with an interest in understanding the evolution of the new media of the public sphere.
Tweeting Out Loud: Prosodic Orthography on Social Media
(2021-06) Heath, Maria
In this dissertation, I examine the connections between orthography and spoken prosody in relation to English CMC (Computer Mediated Communication). Beginning from the theory that certain nonstandard uses of orthography in CMC are intended to convey prosodic information and that their meaning in a text arises as a result of the prosodic interpretation, I designed and implemented a novel production study to test for these associations. The study compares various prosodic features of productions of tweets written with nonstandard orthographic features to productions of minimal pair versions of the tweets across 35 participants. While still limited on its own, when combined with other methods of analysis the production study allows for a great range of prosodic theories to be tested and for unexpected associations to make themselves apparent. Nine specific nonstandard orthographic patterns are examined in this project. First, five different variations on nonstandard capitalization are compared, including All Caps, All Lowercase, Increasing Caps, Word Caps, and Alternating Caps. After determining the typical productions of All Caps and All Lowercase, the three other capitalization patterns are analyzed for similarities to the two basic patterns as well as the overall iconic function of capitalization. The Repeated Letters pattern is the only spelling-based pattern examined, and it is compared in particular to the Word Caps pattern due to the similarity of the localization of the nonstandard orthography to a single word. Finally, three punctuation patterns are examined, including Repeated Question Marks and Repeated Exclamation Points (in comparison to the Repeated Letters pattern) and the Consecutive Punctuation pattern, as a examples of the strength of conventional prosodic associations under nonstandard circumstances. Taken together, these analyses offer support for the use of nonstandard orthography as an indicator of prosody, as well as providing a nuanced and occasionally surprising picture of how nonstandard orthography is interpreted in CMC contexts.
Tweeting the storm: A SCCT approach to NPOs’ Twitter communications during Hurricane Matthew
(2017-05) Tich, Kendall
Hurricane Matthew, one of recent history’s most devastating natural disasters, had a severe impact on parts of the Southeastern U.S. and Haiti. This research looked at how four non-profit organizations, The American Red Cross, The Salvation Army USA, Hope for Haiti, and World Vision Haiti, used Twitter to communicate crisis response strategies with the public. Guided by the SCCT, this study implemented a qualitative textual analysis of the organizations’ Tweets in the pre-crisis, crisis, and post-crisis phases of the disaster. The research findings indicated a disconnect between theoretical response recommendations and Twitter communication. Recommendations for practical implications of this research included a need for greater consideration, on the part of practitioners, organizations, and others involved in crisis communication, of SCCT response recommendations, Twitter as a unique and growing communication outlet, and target audience of response strategies and crisis communication.
Writings on the Wall: The Need for an Authorship-Centric Approach to the Authentication of Social-Networking Evidence
(University of Minnesota. Consortium on Law and Values in Health, Environment & the Life Sciences, 2013-04-15) Robbins, Ira P.
People are stupid when it comes to their online postings. The recent spate of social-networking websites has shown that people place shocking amounts of personal information online. Unlike more traditional modes of communication, the unique nature of these websites allows users to hide behind a veil of anonymity. But while social-networking sites may carry significant social benefits, they also leave users - and their personal information - vulnerable to hacking and other forms of abuse. This vulnerability is playing out in courtrooms across the country and will only increase as social-networking use continues to proliferate. This Article addresses the evidentiary hurdle of authenticating social-networking evidence, a novel legal issue confronting courts today. The Article explains and critiques four approaches used by different jurisdictions, concluding that each approach fails to adequately address the critical issue of authorship. The anonymous nature of social-networking websites, coupled with the extent of users’ personal information available online, raises serious concerns about the authorship of any piece of evidence posted to one of these sites. Litigants are using social-networking postings in court, attributing authorship to a particular person without demonstrating a sufficient nexus between the posting and the purported author. Absent this nexus, however, the evidence fails to meet even the low hurdle of authentication. To remedy this problem, this Article proposes that courts shift their focus from account ownership and content to authorship of the evidence. Working within the existing rules of evidence, this approach underscores the importance of fairness and accuracy in the outcome of judicial proceedings that involve social-networking evidence.

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