Dark side of UGC: A user-centric perspective on the impact of user-generated content
|Authors:||Kariryaa, Ankit||Supervisor:||Schöning, Johannes||1. Expert:||Schöning, Johannes||2. Expert:||Gieseke, Fabian||Abstract:||
User-generated content (UGC) has been on the rise with the emergence of Web 2.0. UGC has led to numerous innovations and has transformed our world in many ways. While the positive impact of UGC is abundant, there is limited research on its negative impact. In this thesis, we study the impact of the UGC from the perspective of the users. This thesis has four main contributions.
First, we study the impact of the UGC on the geo-privacy of the user. We investigate the accuracy of localness methods used for the categorization of UGC at the city, county, and state scale through a user study and highlight its impact on users.
Second, through a study of political communication on Twitter, we analyze the impact of national flags in UGC. Our results show that flags remain an influential symbol in online communication for most political parties in Germany and the USA.
Third, we present a personal password meter for limiting the impact of UGC on the online privacy of the users. Through a user study, we find that our tool significantly limits the inclusion of personal information in passwords, thus limiting the negative impacts of UGC on online security.
Finally, we present a deep learning-based approach for identifying individual trees at a large scale, with which we detect over 1.8 billion individual trees in 1.3 million sq. km area in Western Africa. Our assessment suggests a way to monitor trees outside forests globally and to explore their role in mitigating soil degradation, and climate change. While content generation is associated with some adverse impacts on the user, it also offers an opportunity for large scale UGC-based citizen science platforms. In the future, large scale citizen platforms might be crucial for tackling global challenges such as shrinking biodiversity, and the presented approach could be crucial for bootstrapping such platforms.
|Keywords:||User-generated content; Machine Learning; Human-Computer Interaction; Artificial Intelligence; Social Media; Twitter; Facebook||Issue Date:||28-Sep-2020||Type:||Dissertation||DOI:||10.26092/elib/331||URN:||urn:nbn:de:gbv:46-elib45345||Institution:||Universität Bremen||Faculty:||FB03 Mathematik/Informatik|
|Appears in Collections:||Dissertationen|
checked on Feb 25, 2021
checked on Feb 25, 2021
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