'I See Something You Don't See'. A Computational Analysis of the Digital Services Act and the Digital Markets Act

none4siIn its latest proposals, the Digital Markets Act (DMA) and Digital Services Act (DSA), the European Commission puts forward several new obligations for online intermediaries, especially large online platforms and “gatekeepers.” Both are expected to serve as a blueprint for regulation in the United States, where lawmakers have also been investigating competition on digital platforms and new antitrust laws passed the House Judiciary Committee as of June 11, 2021. This Article investigates whether all stakeholder groups share the same understanding and use of the relevant terms and concepts of the DSA and DMA. Leveraging the power of computational text analysis, we find significant differences in the employment of terms like “gatekeepers,” “self-preferencing,” “collusion,” and others in the position papers of the consultation process that informed the drafting of the two latest Commission proposals. Added to that, sentiment analysis shows that in some cases these differences also come with dissimilar attitudes. While this may not be surprising for new concepts such as gatekeepers or self-preferencing, the same is not true for other terms, like “self-regulatory,” which not only is used differently by stakeholders but is also viewed more favorably by medium and big companies and organizations than by small ones. We conclude by sketching out how different computational text analysis tools, could be combined to provide many helpful insights for both rulemakers and legal scholars.Di Porto, Fabiana; Grote, Tatjana; Volpi, Gabriele; Invernizzi, RiccardoDi Porto, Fabiana; Grote, Tatjana; Volpi, Gabriele; Invernizzi, Riccard

Capture - Upload - Broadcast. A case study in the gatekeeping of amateur footage

This thesis explores the transition of amateur footage across three different stages of the news making process. Through a case study of the 'Mardi Gras Police Brutality Video' this thesis tracks and analyses the development, reception and integration of amateur footage. Each stage is marked by a different media environment, firstly, as an eyewitness to the news event, secondly through its development in the online YouTube landscape and finally in its broadcast across TV news networks. In order analyse each of these media platforms a mixed-method approach has been adopted that utilises both qualitative content analysis and textual analysis. Whilst the thesis is situated in gatekeeping theory it also crosses into other areas of discussion integral to the understanding of the progression of this case study. This includes the concepts of gatewatching, eyewitnessing and participatory journalism. This thesis is an original contribution to the field of gatekeeping theory by focusing on a unique case study and addressing a new component of gatekeeping processes. What happens to amateur footage as it moves through the gates? This thesis argues that despite the proliferation of amateur footage and the multiplying of gates across multiple platforms, Australian TV news networks successfully retain their authority as gatekeepers through a process of normalisation. However, as this thesis will demonstrate, participatory journalists and active audiences in sites such as YouTube now have the power to influence and judge what enters through the gates

Talking at Cross Purposes? A Computational Analysis of the Debate on Informational Duties in the Digital Services and the Digital Markets Acts

none4siSince the opaqueness of algorithms used for rankings, recommender systems, personalized advertisements, and content moderation on online platforms opens the door to discriminatory and anti-competitive behavior, increasing transparency has become a key objective of EU lawmakers. In the latest Commission proposals, the Digital Markets Act and Digital Services Act, transparency obligations for online intermediaries, platforms and ‘gatekeepers’ figure prominently. This paper investigates whether key concepts of competition law and transparency on digital markets are used in the same way by different stakeholders. Leveraging the power of computational text analysis, we find significant differences in the employment of terms like ‘gatekeepers’, ‘simple’, and ‘precise’ in the position papers that informed the drafting of the two latest Commission proposals. This finding is not only informative for the Commission and legal scholars, it might also affect the effectiveness of transparency duties, for which it is often simply assumed that phrases like ‘precise information’ are understood the same way by those implementing said obligations. Hence, it may explain why they fail so often to reach their goal. We conclude by sketching out how different computational text analysis tools, like topic modeling, sentiment analysis and text similarity, could be combined to provide many helpful insights for both rulemakers and the legal scholarship.Di Porto, Fabiana; Grote, Tatjana; Volpi, Gabriele; Invernizzi, RiccardoDi Porto, Fabiana; Grote, Tatjana; Volpi, Gabriele; Invernizzi, Riccard

How much do you care about education? Exploring fluctuations of public interest in education issues among top national priorities in the U.S.

It is well known that a strong education system produces citizens who are more engaged in civil and social duties, with obvious benefits to society and the individuals. Policymakers who have the power to help improve the education system frequently rely on the news or the polls to better understand the issues involved, but these tools are often unable to answer customized questions on the public view with a large enough coverage. Monitoring the American public interest in education over the years is not new. In fact, a number of national polling agencies have tracked education as part of their larger polls asking people to name the most burning issues facing the US. While these polls provide a fair indication of the changes in importance of education in the eyes of the public, they do not identify the factors which have historically been associated with the major fluctuations of such importance. Most importantly, these traditional national polls do not track public concern about specific subtopics within education. This mixed methods study includes the creation of a software instrument with the objective of exploring the salience of education as a national priority over time and analyzing the possible factors associated with these fluctuations of interest. In addition to discovering the most prominent latent subtopics affecting education (such as academic achievement, sexual assault and freedom of speech), this study also seeks national-level issues that may have recently been associated with the largest declines. The only source of data utilized is the text of tens of thousands of published news articles. Terms extracted from the text using natural language processing serve as the basis for automated qualitative analysis. As topics emerge from the data, the frequencies of the terms are utilized to associate the articles with the most relevant ones. The analysis shows that public interest in education has declined the most during election times. It is also found that the areas that contributed the most during the largest surges of public interest in education from 2015 to 2020 were school budget, academic achievement gaps and mental health

Aspect-based Sentiment Analysis for German: Analyzing Talk of Literature" Surrounding Literary Prizes on Social Media

Since the rise of social media, the authority of traditional professional literary critics has beensupplemented – or undermined, depending on the point of view – by technological developmentsand the emergence of community-driven online layperson literary criticism. So far, relatively littleresearch (Allington 2016, Kellermann et al. 2016, Kellermann and Mehling 2017, Bogaert 2017, Pi-anzola et al. 2020) has examined this layperson user-generated evaluative “talk of literature”instead of addressing traditional forms of consecration. In this paper, we examine the layper-son literary criticism pertaining to a prominent German-language literary award: the Ingeborg-Bachmann-Preis, awarded during the Tage der deutschsprachigen Literatur (TDDL).We propose an aspect-based sentiment analysis (ABSA) approach to discern the evaluativecriteria used to differentiate between ‘good’ and ‘bad’ literature. To this end, we collected a cor-pus of German social media reviews, retrieved from Twitter, and enriched it with manual ABSAannotations:aspectsand aspect categories (e.g. the motifs or themes in a text, the jury discus-sions and evaluations, ...),sentiment expressionsandnamed entities. In a next step, the manualannotations are used as training data for our ABSA pipeline including 1) aspect term categoryprediction and 2) aspect term polarity classification. Each pipeline component is developed usingstate-of-the-art pre-trained BERT models.Two sets of experiments were conducted for the aspect polarity detection: one where only theaspect embeddings were used and another where an additional context window of five adjoiningwords in either direction of the aspect was considered. We present the classification results forthe aspect category and aspect sentiment prediction subtasks for the Twitter corpus. Thesepreliminary experimental results show a good performance for the aspect category classification,with a macro and a weighted F1-score of 69% and 83% for the coarse-grained and 54% and 73% forthe fine-grained task, as well as for the aspect sentiment classification subtask, using an additionalcontext window, with a macro and a weighted F1-score of 70% and 71%, respectivel