Political Stance Detection in E stonian News Media (2024)

Abstract: Newspapers have always remained an important medium for disseminating information to the masses. With continuous access and availability of news, there is a severe competition among news media agencies to attract user attention. Therefore, ensuring fairness in news reporting, such as, politically stance neutral reporting has become more crucial than before. Although several research studies have explored and detected political stance in English news articles, there is a lack of research focusing on low-resource languages like Estonian. To address this gap, this paper examines the effectiveness of established stance-detection features that have been successful for English news media, while also proposing novel features tailored specifically for Estonian. Our study consists of 32 different features comprising of lexical, Estonian-specific, framing and sentiment-related features out of which we identify 15 features as useful for stance detection.

## introduction with the rise of the internet, the informationseeking behavior has undergone a shift such that news media has pivoted away from traditional printed newspapers towards social media and online platforms @xcite . furthermore, in today's interconnected world, online news articles becoming readily available within minutes of an event occurring @xcite . while users rely on the news media agencies for a fair and high quality reporting of news events, there has been several instances of deviation from journalistic news values in news reporting @xcite , such as, deliberately lying or leaving out context, not fact-checking sources, using clickbait, being biased @xcite , using politically aligned news reporting @xcite , etc. however, with this massive growth in news media and shift in news consumption behavior, it has become increasingly challenging and time-consuming to manually verify bias in news articles and ensure that the news articles follows journalistic standards. this is especially true for low-resource languages, where building machine learning based solutions is often more difficult due to the lack of training data. therefore, it is important to develop and explore techniques, which use automatically extracted text features as a way to gain insight and monitor news media. although there are several forms of bias in news media, in this paper, we focus on political leaning or stance in news articles. while few automated stance detection concerning political leaning has been explored for different topics in english news media, such as political elections and candidates, climate change, covid-19, and abortion rights, more extensive research is needed to enhance understanding and accuracy @xcite @xcite @xcite @xcite . however, political stance detection in estonian news media is a mostly unexplored topic. estonian, spoken by about a million people, has a much smaller language corpus with around 3 billion words compared to english, which has over 1.4 billion speakers (dyvik) and a corpus of 800 billion words @xcite . meanwhile, the rise of online news media in estonia is significant. for example, delfi meedia, a major estonian media company, has over 700,000 monthly readers and has amassed over 100,000 paid online subscribers by 2023 (delfi meedia; eesti meediaettevõtete liit). with such growth, the need for automated systems to verify news articles and detect political stances is essential. developing automated approaches for lowresource languages can be challenging, as these smaller languages are particularly affected by the non-availability of task and domain-specific data @xcite . furthermore, identifying labeled data requires manual annotation, which is time and cost intensive. compared to english, the data can be of lower quality, which can lead to poorer results and varied performance. therefore, it is particularly challenging to build automated models, especially train large language models, for political stance detection in estonian news media. in this paper, political stance in estonian news media is analyzed on the target of immigration. immigration is a concept that encompasses the international movement of people, usually foreign nationals @xcite . immigration is a suitable target for automated stance detection, as stances towards it are varied and can often veer towards extremes @xcite . immigrants can be viewed as strong and talented workers with great potential or, conversely, burdens on society who take jobs from locals and will not integrate into the local culture @xcite . media coverage of immigration can influence public opinion, especially when it adopts an overly negative stance. these shifts in attitude can potentially translate to negative treatment of immigrants, fueling racism and social division, and the enactment of discriminatory policies @xcite . we explore and identify relevant features and techniques indicative of political stance in estonian news media. the study identifies 15 significant features out of 32 for detecting political stance in estonian news media. sentences opposing immigration are longer, more complex, and used more adjectives and quotes, indicating emotionally charged language. content analysis shows that anti-immigration texts mentioned destinations like sweden and germany, while supportive texts focus on transit countries like greece and turkey, highlighting different framing strategies. estonianspecific features like conditionals and translatives are more prevalent in both supportive and against stances. framing analysis uncovers distinct language use based on stance: negative terms like illegaalne immigrant (illegal immigrant) and neeger (nigger) for opposition, versus more politically neutral ones like aafrika päritolu (african origin) for support, highlighting contrasting frames in legality and humanity. sentiment analysis shows that the xlm-roberta model outperforms others, achieving the highest f1-scores across all stances. the organization of the paper is as follows. section 2 gives an overview of the dataset and describes the preprocessing step followed by the pro-posed methodology in section 3. we discuss the exhaustive analysis of extracted features and their usefulness in political stance detection in section 4 and finally, conclude in section 5. ## dataset we use the dataset described by @xcite who collected 266 628 news articles from two estonian news providers -ekspress grupp foot_0 (the parent company of delfi meedia) and uued uudised foot_1 between 2015 and 2022 on the topic of immigration. the target is immigration, and the text is a topic-related sentence. the dataset comprises of 3261 sentences out of which 1175 sentences are of against stance, 1597 neutral stance, and 489 supportive stance towards immigration. for our study, we consider only the text of the news article. while additional meta-features, such as the title, author, publication date, and publisher, are available, they are not considered as they require prior outside knowledge about a media outlet or author and their stance on specific issues. an overview of the dataset is illustrated in ## methodology in this section, we study different features that can help in identifying the political stance of estonian news media segregated into lexical features, features specific to the estonian language, framingrelated features, and sentiment features. a complete list of all the features used is shown in appendix a. ## results in this section, we discuss the results for features that provides a significant difference in observations results for the different stances. in total, we confirmed 15 features out of total of 32 features (as shown in appendix a) to be useful for political stance detection in estonian news media. all results were tested using a p-test and useful results were confirmed to have a p-value of under 0.01. ## conclusion automated political stance detection in estonian is highly challenging due to the lack of existing datasets and estonian specific language processing tools. in this paper, we study political stance detection with respect to immigration in detail. our analysis comprises of 32 features segregated between lexical features, estonian-specific features, framing-related features and sentiment-related features. these features were exhaustively analyzed to determine their suitability for political stance detection in estonian news media. our observations indicate that 15 features were shown to be helpful in political stance detection. furthermore, to the best of our knowledge, this is the first work that explores novel political stance detection features specific to the estonian language. as a future direction, the rich morphology of estonian could be studied by conducting a comprehensive frequency analysis of all cases and conjugation forms. this could reveal additional features and insights related to stance or sentiment. additionally, there is a need to develop a more extensive multi-domain dataset focused on political stance detection in estonian news, which would support the development of automated machine learning models in this language.