@phdthesis{HannesVincent2022,
  author    = {Hannes-Vincent, Krause},
  title     = {Social networking site use and well-being - a nuanced understanding of a complex relationship},
  school      = {Universit{\"a}t Potsdam},
  year      = {2022},
  abstract  = {Social Networking Sites (SNSs) are ubiquitous and attract an enormous chair of the digital population. Their functionalities allow users to connect and interact with others and weave complex social networks in which social information is continuously disseminated between users. Besides the social value SNSs are generating, they likewise attract companies and allow for new forms of marketing, thereby creating considerable economic value alike. However, as SNSs grew in popularity, so did concerns about the impact of their use on social interactions in general and the well-being of individual users in particular. While existing scientific evidence points to both risk as well as benefits of SNS use, research still lacks a profound understanding of which aspects of SNSs enable an impact on well-being and which psychological processes on the part of the users underly and explain this relationship. Therefore, this thesis is dedicated to an in-depth exploration of the relationship between SNS use and well-being and aims to answer how SNS use can impact well-being. Primarily, it focuses on the unique technological features that characterize SNSs and enable potential well- being alterations and on specific psychological processes on the part of the users, underlying and explaining the relationship. For this purpose, the thesis first introduces the concept of well- being. It continues by presenting SNSs' unique technological features, divided into specifics of the content disseminated on SNSs and the network structure of SNSs. Further, the thesis introduces three classes of psychological processes assumed most relevant for the relationship between SNSs and well-being: other-focused, self-focused, and contrastive processes.. It is assumed that the course and quality of these common processes change in the SNS context and that a complex interplay between the unique features of SNSs and these processes determines how SNSs may ultimately affect users' well-being - both in positive and negative ways. The dissertation comprises seven research articles, each of which focusses on a particular set of SNS characteristics, their interplay with one or more of the proposed psychological processes, and ultimately the resulting effects on user well-being or its key resilience and risk factors. The seven articles investigate this relationship using different methodological approaches. Three articles are based on either systematic or narrative literature reviews, one applies an empirical cross-sectional research design, and three articles present an experimental investigation. Thematically, two articles revolve around SNS use's effect on self-esteem. Three articles examine the specific role of the emotion of envy and its potential to establish and perpetuate a well-being-damaging social climate on SNSs. The two last articles of this thesis revolve around the established assumption that active and passive SNS use, as different modalities of SNS use, cause differential effects on users' well-being due to the involvement of different psychological processes. The results of this thesis illustrate different ways how SNSs can affect users' well-being. The results suggest that especially contrastive processes play a decisive role in explaining potential well-being risks for SNS users. Their interplay with certain SNS features seems to foster upward social comparisons and feelings of envy, potentially leading to a complex set of deleterious effects on users' well-being. At the same time, the findings illuminate ways in which SNSs can benefit users and their self-esteem - especially when SNS use promotes self- focused and social-feedback-based other-focused processes. The thesis and their findings illustrate that the relationship between SNSs and well-being is complex. Therefore, a nuanced perspective, taking into consideration both the technological uniqueness of SNSs and the psychological processes they are enabling, is crucial to understand how these technologies affect their users in good and potentially harmful ways. On the one hand, the gathered insights contribute to research, providing novel insights into the complex relationship between SNS use and well-being. On the other hand, the results enable a focused and action-oriented derivation of recommendations for stakeholders such as individual users, policymakers, and platform providers. The findings of this thesis can help them to better combat SNS-related risks and ultimately ensure a healthy and sustainable environment for users - and thus also the economic values of SNSs - in the long term.},
  language  = {en}
}
@phdthesis{BinTareaf2022,
  author    = {Bin Tareaf, Raad},
  title     = {Social media based personality prediction models},
  doi       = {10.25932/publishup-54914},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-549142},
  school      = {Universit{\"a}t Potsdam},
  pages     = {x, 137},
  year      = {2022},
  abstract  = {Individuals have an intrinsic need to express themselves to other humans within a given community by sharing their experiences, thoughts, actions, and opinions. As a means, they mostly prefer to use modern online social media platforms such as Twitter, Facebook, personal blogs, and Reddit. Users of these social networks interact by drafting their own statuses updates, publishing photos, and giving likes leaving a considerable amount of data behind them to be analyzed. Researchers recently started exploring the shared social media data to understand online users better and predict their Big five personality traits: agreeableness, conscientiousness, extraversion, neuroticism, and openness to experience. This thesis intends to investigate the possible relationship between users' Big five personality traits and the published information on their social media profiles. Facebook public data such as linguistic status updates, meta-data of likes objects, profile pictures, emotions, or reactions records were adopted to address the proposed research questions. Several machine learning predictions models were constructed with various experiments to utilize the engineered features correlated with the Big 5 Personality traits. The final predictive performances improved the prediction accuracy compared to state-of-the-art approaches, and the models were evaluated based on established benchmarks in the domain. The research experiments were implemented while ethical and privacy points were concerned. Furthermore, the research aims to raise awareness about privacy between social media users and show what third parties can reveal about users' private traits from what they share and act on different social networking platforms. In the second part of the thesis, the variation in personality development is studied within a cross-platform environment such as Facebook and Twitter platforms. The constructed personality profiles in these social platforms are compared to evaluate the effect of the used platforms on one user's personality development. Likewise, personality continuity and stability analysis are performed using two social media platforms samples. The implemented experiments are based on ten-year longitudinal samples aiming to understand users' long-term personality development and further unlock the potential of cooperation between psychologists and data scientists.},
  language  = {en}
}
@phdthesis{Sidarenka2019,
  author    = {Sidarenka, Uladzimir},
  title     = {Sentiment analysis of German Twitter},
  doi       = {10.25932/publishup-43742},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-437422},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vii, 217},
  year      = {2019},
  abstract  = {The immense popularity of online communication services in the last decade has not only upended our lives (with news spreading like wildfire on the Web, presidents announcing their decisions on Twitter, and the outcome of political elections being determined on Facebook) but also dramatically increased the amount of data exchanged on these platforms. Therefore, if we wish to understand the needs of modern society better and want to protect it from new threats, we urgently need more robust, higher-quality natural language processing (NLP) applications that can recognize such necessities and menaces automatically, by analyzing uncensored texts. Unfortunately, most NLP programs today have been created for standard language, as we know it from newspapers, or, in the best case, adapted to the specifics of English social media. This thesis reduces the existing deficit by entering the new frontier of German online communication and addressing one of its most prolific forms—users' conversations on Twitter. In particular, it explores the ways and means by how people express their opinions on this service, examines current approaches to automatic mining of these feelings, and proposes novel methods, which outperform state-of-the-art techniques. For this purpose, I introduce a new corpus of German tweets that have been manually annotated with sentiments, their targets and holders, as well as lexical polarity items and their contextual modifiers. Using these data, I explore four major areas of sentiment research: (i) generation of sentiment lexicons, (ii) fine-grained opinion mining, (iii) message-level polarity classification, and (iv) discourse-aware sentiment analysis. In the first task, I compare three popular groups of lexicon generation methods: dictionary-, corpus-, and word-embedding-based ones, finding that dictionary-based systems generally yield better polarity lists than the last two groups. Apart from this, I propose a linear projection algorithm, whose results surpass many existing automatically-generated lexicons. Afterwords, in the second task, I examine two common approaches to automatic prediction of sentiment spans, their sources, and targets: conditional random fields (CRFs) and recurrent neural networks, obtaining higher scores with the former model and improving these results even further by redefining the structure of CRF graphs. When dealing with message-level polarity classification, I juxtapose three major sentiment paradigms: lexicon-, machine-learning-, and deep-learning-based systems, and try to unite the first and last of these method groups by introducing a bidirectional neural network with lexicon-based attention. Finally, in order to make the new classifier aware of microblogs' discourse structure, I let it separately analyze the elementary discourse units of each tweet and infer the overall polarity of a message from the scores of its EDUs with the help of two new approaches: latent-marginalized CRFs and Recursive Dirichlet Process.},
  language  = {en}
}
@phdthesis{Risch2020,
  author    = {Risch, Julian},
  title     = {Reader comment analysis on online news platforms},
  doi       = {10.25932/publishup-48922},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-489222},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xi, 135},
  year      = {2020},
  abstract  = {Comment sections of online news platforms are an essential space to express opinions and discuss political topics. However, the misuse by spammers, haters, and trolls raises doubts about whether the benefits justify the costs of the time-consuming content moderation. As a consequence, many platforms limited or even shut down comment sections completely. In this thesis, we present deep learning approaches for comment classification, recommendation, and prediction to foster respectful and engaging online discussions. The main focus is on two kinds of comments: toxic comments, which make readers leave a discussion, and engaging comments, which make readers join a discussion. First, we discourage and remove toxic comments, e.g., insults or threats. To this end, we present a semi-automatic comment moderation process, which is based on fine-grained text classification models and supports moderators. Our experiments demonstrate that data augmentation, transfer learning, and ensemble learning allow training robust classifiers even on small datasets. To establish trust in the machine-learned models, we reveal which input features are decisive for their output with attribution-based explanation methods. Second, we encourage and highlight engaging comments, e.g., serious questions or factual statements. We automatically identify the most engaging comments, so that readers need not scroll through thousands of comments to find them. The model training process builds on upvotes and replies as a measure of reader engagement. We also identify comments that address the article authors or are otherwise relevant to them to support interactions between journalists and their readership. Taking into account the readers' interests, we further provide personalized recommendations of discussions that align with their favored topics or involve frequent co-commenters. Our models outperform multiple baselines and recent related work in experiments on comment datasets from different platforms.},
  language  = {en}
}
@phdthesis{SantosBruss2020,
  author    = {Santos Bruss, Sara Morais dos},
  title     = {Feminist solidarities after modulation},
  publisher = {punctum books},
  address   = {Brooklyn, NY},
  isbn      = {978-1-68571-146-7},
  doi       = {10.53288/0397.1.00},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiii, 380},
  year      = {2020},
  abstract  = {Feminist Solidarities after Modulation produces an intersectional analysis of transnational feminist movements and their contemporary digital frameworks of identity and solidarity. Engaging media theory, critical race theory, and Black feminist theory, as well as contemporary feminist movements, this book argues that digital feminist interventions map themselves onto and make use of the multiplicity and ambiguity of digital spaces to question presentist and fixed notions of the internet as a white space and technologies in general as objective or universal. Understanding these frameworks as colonial constructions of the human, identity is traced to a socio-material condition that emerges with the modernity/colonialism binary. In the colonial moment, race and gender become the reasons for, as well as the effects of, technologies of identification, and thus need to be understood as and through technologies. What Deleuze has called modulation is not a present modality of control, but is placed into a longer genealogy of imperial division, which stands in opposition to feminist, queer, and anti-racist activism that insists on non-modular solidarities across seeming difference. At its heart, Feminist Solidarities after Modulation provides an analysis of contemporary digital feminist solidarities, which not only work at revealing the material histories and affective ""leakages"" of modular governance, but also challenges them to concentrate on forms of political togetherness that exceed a reductive or essentialist understanding of identity, solidarity, and difference.},
  language  = {en}
}