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Why isn't Google welcome in Kreuzberg? Social movement and the effects of Internet on urban space
(2020)
Advances in information and communication technologies such as the Internet have driven a great transformation in the interactions between individuals and the urban environment. As the use of the Internet in cities becomes more intense and diverse, there is also a restructuring of urban space, which is experienced by groups in society in various ways, according to the specificity of each context. Accordingly, large Internet companies have emerged as new players in the processes of urbanization, either through partnerships with the public administration or through various services offered directly to urban residents. Once these corporations are key actors in the digitalization of urban services, their operations can affect the patterns of urban inequality and generate a series of new struggles over the production of space. Interested in analyzing this phenomena from the perspective of civil society, the present Master Thesis examined a social movement that prevented Google to settle a new startup campus in the district of Kreuzberg, in Berlin. By asking why Google was not welcome in that context, this study also sought to understand how internet, as well as its main operators, has affected everyday life in the city. Thus, besides analyzing the movement, I investigated the particularities of the urban context where it arose and the elements that distinguish the mobilization’s opponent. In pursuit of an interdisciplinary approach, I analyzed and discussed the results of empirical research in dialogue with critical theories in the fields of urban studies and the Internet, with emphasis on Castells' definitions of urban social movements and network society (1983, 2009, 2015), Couldry's and Mejias' (2019) idea of data colonialism, Lefèbvre's (1991, 1996) concepts of abstract space and the right to the city, as well as Zuboff's (2019) theory of surveillance capitalism. The case at hand has exposed that Google plays a prominent role in the way the Internet has been developed and deployed in cities. From the perspective accessed, the current appropriation of Internet technologies has been detrimental to individual autonomy and has contributed to intensifying existing inequalities in the city. The alternative vision to this relies mainly on the promotion of decentralized solidarity networks.
With the growing importance of the World Wide Web, the major challenges our society faces are also increasingly affecting the digital areas of our lives. Some of the associated problems can be addressed by computer science, and some of these specifically by data-driven research. To do so, however, requires to solve open issues related to archive quality and the large volume and variety of the data contained.
This dissertation contributes data, algorithms, and concepts towards leveraging the big data and temporal provenance capabilities of web archives to tackle societal challenges. We selected three such challenges that highlight the central issues of archive quality, data volume, and data variety, respectively:
(1) For the preservation of digital culture, this thesis investigates and improves the automatic quality assurance of the web page archiving process, as well as the further processing of the resulting archive data for automatic analysis.
(2) For the critical assessment of information, this thesis examines large datasets of Wikipedia and news articles and presents new methods for automatically determining quality and bias.
(3) For digital security and privacy, this thesis exploits the variety of content on the web to quantify the security of mnemonic passwords and analyzes the privacy-aware re-finding of the various seen content through private web archives.
Search engines are very good at answering queries that look for facts. Still, information needs that concern forming opinions on a controversial topic or making a decision remain a challenge for search engines. Since they are optimized to retrieve satisfying answers, search engines might emphasize a specific stance on a controversial topic in their ranking, amplifying bias in society in an undesired way. Argument retrieval systems support users in forming opinions about controversial topics by retrieving arguments for a given query. In this thesis, we address challenges in argument retrieval systems that concern integrating them in search engines, developing generalizable argument mining approaches, and enabling frame-guided delivery of arguments.
Adapting argument retrieval systems to search engines should start by identifying and analyzing information needs that look for arguments. To identify questions that look for arguments we develop a two-step annotation scheme that first identifies whether the context of a question is controversial, and if so, assigns it one of several question types: factual, method, and argumentative. Using this annotation scheme, we create a question dataset from the logs of a major search engine and use it to analyze the characteristics of argumentative questions. The analysis shows that the proportion of argumentative questions on controversial topics is substantial and that they mainly ask for reasons and predictions. The dataset is further used to develop a classifier to uniquely map questions to the question types, reaching a convincing F1-score of 0.78.
While the web offers an invaluable source of argumentative content to respond to argumentative questions, it is characterized by multiple genres (e.g., news articles and social fora). Exploiting the web as a source of arguments relies on developing argument mining approaches that generalize over genre. To this end, we approach the problem of how to extract argument units in a genre-robust way. Our experiments on argument unit segmentation show that transfer across genres is rather hard to achieve using existing sequence-to-sequence models.
Another property of text which argument mining approaches should generalize over is topic. Since new topics appear daily on which argument mining approaches are not trained, argument mining approaches should be developed in a topic-generalizable way. Towards this goal, we analyze the coverage of 31 argument corpora across topics using three topic ontologies. The analysis shows that the topics covered by existing argument corpora are biased toward a small subset of easily accessible controversial topics, hinting at the inability of existing approaches to generalize across topics. In addition to corpus construction standards, fostering topic generalizability requires a careful formulation of argument mining tasks. Same side stance classification is a reformulation of stance classification that makes it less dependent on the topic. First experiments on this task show promising results in generalizing across topics.
To be effective at persuading their audience, users of an argument retrieval system should select arguments from the retrieved results based on what frame they emphasize of a controversial topic. An open challenge is to develop an approach to identify the frames of an argument. To this end, we define a frame as a subset of arguments that share an aspect. We operationalize this model via an approach that identifies and removes the topic of arguments before clustering them into frames. We evaluate the approach on a dataset that covers 12,326 frames and show that identifying the topic of an argument and removing it helps to identify its frames.