Towards Automated Frame Analysis : Natural Language Processing Techniques to Reveal Media Bias in News Articles

Abstract

News articles serve as a highly relevant source for individuals to inform themselves on current topics and salient political issues. How the news covers an issue decisively affects public opinion and our collective decision-making. Albeit the news is meant to not only communicate "objective facts" but also to assess events and their implications, biased coverage can be problematic. Especially when news consumers are not aware of the often subtle yet powerful slants present in the news, or when coverage is systematically slanted to alter public opinion, media bias poses a severe problem to society. Empowering newsreaders to critically assess the news is an essential means to face the issues caused by media bias. On the one hand, non-technical means such as media literacy practices or analysis approaches devised in political science are highly effective. However, they often come with immense efforts, such as researching and contrasting relevant news articles. Ultimately, this effort can represent an insurmountable barrier for these manual techniques to be applied during daily news consumption. On the other hand, automated data analysis methods are available and could enable timely bias analysis. However, automated approaches largely neglect the sophisticated models and analysis approaches devised in decade-long bias research in the social sciences. Compared to them, the automated approaches often yield superficial or inconclusive results. To enable effective and efficient bias identification, the thesis at hand proposes an interdisciplinary approach to reveal biases in English news articles reporting on a given political event. Therefore, the approach identifies the coverage's different perspectives on the event. The approach's so-called person-oriented frames represent how articles portray the persons involved in the event. In contrast to prior automated approaches, the identified frames are meaningful and substantially present in the news coverage. In particular, this thesis makes the following research contributions. The thesis presents the first interdisciplinary literature review on approaches for analyzing media bias, thereby contrasting studies and models from the social sciences with automated approaches such as devised in computer science. A key finding is that research in either discipline could benefit from integrating the other's expertise and methods. To facilitate such interdisciplinary research, the thesis establishes a shared conceptual understanding by mapping the state of the art from the social sciences to a framework that automated approaches can target. To address the weaknesses of prior work, the thesis then proposes person-oriented framing analysis (PFA). The approach integrates methodology that has been applied in practice in social science research and identifies specific in-text means of narrowly defined bias forms. In contrast to prior automated approaches, which treat bias rather as a holistic, vague concept, PFA detects article groups representing meaningful frames. Such frames could previously be only identified through manual content analysis or expert knowledge on the analyzed topic. Afterward, the thesis proposes methods for the PFA approach, investigates their suitability concerning PFA, and evaluates their technical effectiveness. For example, the thesis introduces the first method to classify sentiment in news articles. The thesis also lays out essential preparatory work for other tasks. For example, a method is proposed that resolves highly event-specific coreferences, which may even be of contradictory meanings in other contexts, such as "freedom fighters" and "terrorists." To demonstrate the effectiveness of the PFA approach, a prototype system to reveal person-oriented framing in event coverage is presented and evaluated. The results of a user study (n=160) demonstrate the effectiveness of the interdisciplinary approach devised in this thesis. In the study's single-blind setting, the PFA approach is most effective in increasing respondents' bias-awareness. Moreover, the study results confirm the findings of the literature review. They suggest that prior bias identification and communication approaches identify biases that are technically significant but often are meaningfully irrelevant. In practical terms, prior work facilitates the visibility of potential biases, whereas the PFA approach identifies meaningful biases indeed present in the coverage. This thesis is motivated by my vision to mitigate media bias's severely adverse effects on societies. Outside the academic context, this vision entails that popular news aggregators and news apps will integrate effective approaches for bias identification, such as PFA, to help news readers critically assess news coverage in a practical, effortless way during daily news consumption.