Political Bias Research Articles

Maritime Security: Lost in Translation? Developing a Common Lexicon to Enhance Data Integrity and Global Maritime Domain Awareness

With the proliferation of post-Cold War globalisation, the international community began to appreciate the immense importance of maritime security. In recent decades, numerous national, regional, and global information sharing centres have been established with an aim to provide relevant stakeholders with an assessment of the risks and threats within the maritime domain. Unfortunately, international organisations failed to simultaneously emphasise the importance of standardised terminology and definitions for use in the global maritime security context. Instead of using other standardised areas of the maritime industry such as maritime safety as a guide, dozens of government agencies, regional reporting centres, private maritime security companies, and information sharing/fusion centres have now been established. Each of these uses its own unique categories, terminology, and definitions to measure and catalogue regional and global incidents of violent maritime crime. In this article, that includes acts of piracy and armed robbery against ships. In some cases, political and economic bias has played a significant role in how maritime security incident data is officially recorded and presented to the public. This lack of data cohesion has resulted in significant confusion and distrust amongst the shipping industry and policymakers. It has also created an uncertain environment for seafarers, who largely depend on their employers and regulators to keep them safe and secure at sea. The research includes an analysis of several sets of qualitative and quantitative data from prominent agencies and reporting centres. Case studies are also presented to illustrate why harmonised terminology, definitions, and incident reporting protocols are integral to achieving global maritime domain awareness. The numerous challenges to achieving this goal are then explored and proposed solutions are presented. The paper concludes by proposing a lexicon for incidents of violent maritime crime, as a first step in creating a common lexicon for global maritime security. As stated in earlier issues, this journal does not seek to promote a common definition of maritime security, but a common lexicon is of more practical use. This article is based on a dissertation completed as part of the master’s degree in Maritime Security at Coventry University in 2022.

Psychology as Science and as Propaganda

The target article highlights research known to have promoted unjustified politicized claims. It also points out that, although researcher political biases might account for this, there are often alternative explanations. It then discusses areas of research in which those alternative explanations are unlikely, so that the best explanation is political bias. The target article is fundamentally correct. Nonetheless, we argue that political bias is a characteristic of the claims made in research articles rather than primarily a characteristic of scientists. Inasmuch as some claim is not wrong simply by virtue of supporting an ideological narrative, to detect politically biased research, we identify four questions to be answered. Test 0 is necessary but not sufficient to infer political bias. If Test 0 is passed, then at least one of Tests 1, 2, or 3 must also be passed. Test 0: Does the study vindicate some political narrative? Test 1: Did they misinterpret or misrepresent their results in ways that unjustifiably advance a particular politicized narrative? Test 2: Do the authors systematically ignore papers and studies inconsistent with their ideology-affirming conclusions? Test 3: Did they leap to ideology-affirming conclusions based on weak data? We close with recommendations for preventing politically biased conclusions.

Partisan Biases among the Unemployed

Abstract Unemployed people are relatively rarely studied in political science. Yet, with their economic significance and centrality to many political debates, they can provide insight on many questions, including just how far partisan biases – where opinions and even factual perceptions follow what reflects well on their holder's preferred political party – extend. The economic and emotional costs of joblessness make its evaluation an unlikely seeming case for partisan effects. Surveys in the United States and Great Britain nevertheless show that partisan alignment predicts unemployed individuals' evaluation of their economic situation: unemployed individuals identifying with parties represented in the national executive report more positively on their household finances (and on the national economic situation) than do non-partisans, while those identifying with the opposition report more negatively. These effects are especially substantial among people interested in politics. Even something as personal and affectively intense as unemployment is viewed through a partisan scrim.

Economists’ Political Donations and GDP Forecast Accuracy

We examine the association between economists’ political donations and the accuracy of Gross Domestic Product (GDP) forecasts. Prior research suggests that individual political donations reflect personal political orientations, leading to a partisan bias. Therefore, economists might not objectively interpret information, with a subsequent reduction in GDP forecast accuracy. Using a sample of one-quarter-ahead US GDP growth forecasts for 2003–2020, we find that economists making political donations are more accurate than their peers. This result suggests that despite the potential partisan bias, individual political donations give economists an information advantage in predicting GDP growth. We also document that the informational benefits associated with donations are stronger when the political party financed by economists controls both the Senate and House of Representatives. However, these benefits are reduced in periods of uncertainty and when the economists’ forecasting houses are more politically active.

Researching ideologies

Abstract Previous critical studies of language textbook analysis have explored politics of content selection in textbooks, examining the way selection of certain materials instead of others or the interaction between various multimodal contents selected for textbooks reinforces certain ideological meanings at the expense of certain others. The current study views language textbooks through lenses of politics of content creation, analyzing such politics in a series of English as a Foreign Language textbooks produced by Iranian Ministry of Education. Using a theoretical framework that draws on theories of language ideology and social semiotic theories of multimodality, the study explores the way ideological meanings are simultaneously created at different orders of language and the way such created meanings multimodally shape different contents of the textbooks, such as lessons and learning activities. The study contributes to the filed of critical language textbook analysis by uncovering the relation between power and ideologies within the generic structure of language textbooks.

Liberal Bias on Psychology: A Comment on Bartels (2023)

Several prior empirical studies have documented considerable mistakes, often ongoing, in how research is presented in introductory psychology textbooks. Extending upon these previous analyses, Bartels notes that introductory psychology textbooks also demonstrate a pronounced liberal/progressive bias. Given the pronounced ideological bias among both practitioners and researchers in the field, this should not be a surprise. In this comment, I extend Bartels’ observations by raising the issues with textbooks reflect widespread problems within our field. It is well past time that psychology takes a hard look at how to increase rigor and reduce political biases.

More human than human: measuring ChatGPT political bias

We investigate the political bias of a large language model (LLM), ChatGPT, which has become popular for retrieving factual information and generating content. Although ChatGPT assures that it is impartial, the literature suggests that LLMs exhibit bias involving race, gender, religion, and political orientation. Political bias in LLMs can have adverse political and electoral consequences similar to bias from traditional and social media. Moreover, political bias can be harder to detect and eradicate than gender or racial bias. We propose a novel empirical design to infer whether ChatGPT has political biases by requesting it to impersonate someone from a given side of the political spectrum and comparing these answers with its default. We also propose dose-response, placebo, and profession-politics alignment robustness tests. To reduce concerns about the randomness of the generated text, we collect answers to the same questions 100 times, with question order randomized on each round. We find robust evidence that ChatGPT presents a significant and systematic political bias toward the Democrats in the US, Lula in Brazil, and the Labour Party in the UK. These results translate into real concerns that ChatGPT, and LLMs in general, can extend or even amplify the existing challenges involving political processes posed by the Internet and social media. Our findings have important implications for policymakers, media, politics, and academia stakeholders.

Digital Pseudo-Identification in the Post-Truth Era: Exploring Logical Fallacies in the Mainstream Media Coverage of the COVID-19 Vaccines

Because of China’s new wave of COVID-19 in May 2023, the issue of tackling COVID-19 misinformation remains relevant. Based on Lippmann’s theory of public opinion and agenda setting theory, this article aims to examine the concept of digital pseudo-identification as a type of logical fallacy that refers to supporting journalists’ opinions with ‘false’ arguments that lack factual evidence. To do so, the study applied computer-aided content analysis, as well as rhetorical and critical discourse analyses, to examine 400 articles related to four COVID-19 vaccines (‘Oxford-AstraZeneca’, ‘Pfizer-BioNTech’, ‘Sputnik V’ and ‘Sinovac’) published on the online versions of two major British and American mainstream media sources between August 2020 and December 2021. The results of the study show that journalists of the ‘The New York Times’ and ‘The Guardian’ used similar logical fallacies, including the opinions of pseudo-authorities and references to pseudo-statistics and stereotypes, which contributed to creating distorted representations of the COVID-19 vaccines and propagating online misinformation. The study also reveals political bias in both of the mainstream media sources, with relatively more positive coverage of the European vaccines than non-European vaccines. The findings have important implications for journalism and open up perspectives for further research on the concept of digital pseudo-identification in the humanities and social sciences.

The Dangers of Bad History as a Source of Liberal Bias: Comment on Winegard et al. (2023)

Many social science researchers are liberals and progressives. Many published research studies also happen to support liberal and progressive narratives. This is even true for published research articles which might be fairly interpreted as insulting of conservatives such as referring to them as racist or unintelligent. Is this a coincidence? In a series of impressive studies, Winegard et al. (2023) demonstrate that political bias influences liberals’ perceptions and that, in the quest for finding equality, liberals assign greater moral worth to minority groups than majority. These findings have important implications for recent revisionist history approaches within education, and potential misinformation spread among youth in schools.

Truth sensitivity and partisan bias in responses to misinformation.

Misinformation represents one of the greatest challenges for the functioning of societies in the information age. Drawing on a signal-detection framework, the current research investigated two distinct aspects of misinformation susceptibility: truth sensitivity, conceptualized as accurate discrimination between true and false information, and partisan bias, conceptualized as lower acceptance threshold for ideology-congruent information compared to ideology-incongruent information. Four preregistered experiments (n = 2,423) examined (a) truth sensitivity and partisan bias in veracity judgments and decisions to share information and (b) determinants and correlates of truth sensitivity and partisan bias in responses to misinformation. Although participants were able to distinguish between true and false information to a considerable extent, sharing decisions were largely unaffected by actual information veracity. A strong partisan bias emerged for both veracity judgments and sharing decisions, with partisan bias being unrelated to the overall degree of truth sensitivity. While truth sensitivity increased as a function of cognitive reflection during encoding, partisan bias increased as a function of subjective confidence. Truth sensitivity and partisan bias were both associated with misinformation susceptibility, but partisan bias was a stronger and more reliable predictor of misinformation susceptibility than truth sensitivity. Implications and open questions for future research are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

YouTube's recommendation algorithm is left-leaning in the United States.

With over two billion monthly active users, YouTube currently shapes the landscape of online political video consumption, with 25% of adults in the United States regularly consuming political content via the platform. Considering that nearly three-quarters of the videos watched on YouTube are delivered via its recommendation algorithm, the propensity of this algorithm to create echo chambers and deliver extremist content has been an active area of research. However, it is unclear whether the algorithm may exhibit political leanings toward either the Left or Right. To fill this gap, we constructed archetypal users across six personas in the US political context, ranging from Far Left to Far Right. Utilizing these users, we performed a controlled experiment in which they consumed over eight months worth of videos and were recommended over 120,000 unique videos. We find that while the algorithm pulls users away from political extremes, this pull is asymmetric, with users being pulled away from Far Right content stronger than from Far Left. Furthermore, we show that the recommendations made by the algorithm skew left even when the user does not have a watch history. Our results raise questions on whether the recommendation algorithms of social media platforms in general, and YouTube, in particular, should exhibit political biases, and the wide-reaching societal and political implications that such biases could entail.

A commonsense-infused language-agnostic learning framework for enhancing prediction of political bias in multilingual news headlines

Predicting the political bias of news headlines is a challenging task that becomes even more challenging in a multilingual setting with low-resource languages. To deal with this, we propose to utilise Inferential Commonsense Knowledge via a Translate–Retrieve–Translate strategy to introduce a learning framework. To begin with, we use the translate–retrieve–translate strategy to acquire inferential knowledge in the target language. We then employ an attention mechanism to emphasise important inferences. We finally integrate the attended inferences into a multilingual, pre-trained language model for the task of bias prediction. To evaluate the effectiveness of our framework, we present a dataset of over 62.6K multilingual news headlines annotated with their respective political biases in five low-resource European languages. We evaluate several state-of-the-art multilingual pre-trained language models since their performance tends to vary across languages (low or high resource). Evaluation results demonstrate that our proposed framework is effective regardless of the models employed. Overall, the best-performing model trained with only headlines shows 0.90 accuracy and F1 and a 0.83 Jaccard score. With attended knowledge in our framework, the same model shows an increase in 2.2% accuracy and F1 and a 3.6% Jaccard score. Extending our experiments to individual languages reveals that the models we analyse for Slovenian perform significantly worse than other languages in our dataset. To investigate this, we assess the effect of translation quality on prediction performance. It indicates that the disparity in performance is most likely due to poor translation quality. We release our dataset and scripts at https://github.com/Swati17293/KG-Multi-Bias for future research. Our framework has the potential to benefit journalists, social scientists, news producers, and consumers.

Rethinking Asian values in journalism: the case of the pressroom in the South Korean presidential office

ABSTRACT The present study explored how Asian values are reproduced in South Korean journalistic practices. In-depth interviews were conducted with journalists covering the Korean presidential office. The results revealed that Asian values prioritizing harmony, authority, national interests, and order influence the pressroom of the presidential office. The reporters run dozens of mobile-messenger-based groups, called kumis, with fellow reporters to share information and jointly contact officials. This practice embodies an Asian value – in-group harmony, derived from Confucian communalism. The beat (areas covered by reporters) and pressroom system institutionalizes Asian values by allowing only registered reporters to access the presidential office. The president’s staff abuses off-the-records and embargoes, but the reporters accept them out of respect for the president’s authority. The reporters often make headlines on the president’s national security agenda. We also found the hybridity of Asian values and watchdogs in the Korean reporters’ practices. Partisan bias is a mediator determining whether to activate Asian values or watchdog roles. Asian values are in excess between ideologically homogenous reporters and presidents, while government watchdogs are stimulated between heterogeneous pairs. As press freedom grows in Asia, the model of blending Asian values with other journalistic attributes is likely to be found often.

SOCIOECONOMIC POLICY EVALUATION: PARTISANSHIP AND PARTISAN BIAS EFFECT

Public support of socioeconomic reforms is one of the most important factors for their successful implementation, and conversely, lack of public approval doesn’t allow achieving the expected effect and may result in negative externalities. In this regard, it is relevant to study the origins of people’s opinions and factors affecting their evaluation of social and economic policies. This paper is devoted to the following aspects of this multidimensional problem as partisanship and partisan bias. Their research has been actively promoted by foreign scientists, but so far presented extremely limited in Ukrainian literature. In this paper the critical review of foreign scientific papers devoted to this topic is presented; the main hypotheses about the influence of party identification on public opinion and the effect of partisan bias are highlighted; features of the methodology are noted and research results are summarized. The methodology used by foreign specialists for research of this problem is based on experimental methods, among them the main place belongs to laboratory experiments. A laboratory experiment means observing people’s behavior (participants’ behavior) under strictly controlled conditions when the influence of uncontrolled factors is limited to a minimum. In foreign studies of partisan bias and its influence on citizens' evaluation of socioeconomic policies, laboratory experiments serve as the main tool for testing hypotheses. The results of the research give evidence, on the one hand, that the partisan bias mechanism provides wide possibilities to manipulate the people’s opinion and public opinion in general, on the other hand, that correct objective information about the policy content and goals can significantly weaken the partisan bias effect. Foreign studies prove that party signal influence depends on the people’s properties, policy content, political systems, and party characteristics. In Ukraine, there is a lack its own research in this field, however, as democratic institutions, civil society, and people’s political consciousness and activity develop, these studies become more relevant to the political and economic life in Ukrainian society.

Regime Support and Gender Quotas in Autocracies

Gender quotas are increasingly being adopted by autocrats in part to legitimize their rule. Yet, even in autocracies, these quotas increase women’s political representation. It thus stands to reason that public support for gender quotas in autocracies might be shaped by this trade-off between advancing women’s rights and granting the regime legitimacy. All else equal, regime opponents should be less supportive of gender quotas in autocracies, wary of legitimizing the regime. We uncover evidence of this proposition in an analysis of region-wide Arab Barometer surveys and a survey experiment in Algeria. We also find that evaluations of this trade-off are conditioned by other demographics, with women, gender egalitarians, and Islamists remaining more consistent in their support for/opposition to gender quotas regardless of regime gains. Overall, our findings suggest that gender quotas in autocracies are viewed through a political lens, creating a potential backlash toward women’s empowerment.

News media and public perceptions of police misconduct: Does racial empathy matter?

ABSTRACT High-profile instances of police violence, including the killings of George Floyd and Breonna Taylor, have received immense attention in the news media and brought public attention to police use of force and racial bias. This study examines the connection between news media consumption and public perceptions of police misconduct. It also explores the role of racial empathy, a positive out-group attitude, in mediating this relationship. Analyses of nationally representative survey data indicate that those who view online news sites and those who use Twitter are more likely to believe that police treat White people better than they treat Black people. The political bias of the news site also matters, as those who view right-leaning sites are less likely to believe that police use more force than necessary and act in a biased manner. Racial empathy plays a mediating role in the relationship between viewing news sites and perceptions of police misconduct. Implications for research and policy are discussed.

Political Bias against Atheists: Talk Shows Targeting Arabic-Speaking Audiences

Atheism has stirred up controversy in the Arabic-speaking world since the 2011 uprisings, when atheists there began appearing in public. What role does Arabic mass media play in the modern politics of minorities such as atheists, given the heated debates that it hosts on atheism? This question is important because perceptions of media frames influence the behavior of politicians and the electorate—and, as a result, laws that affect minority groups such as atheists. This article focuses on Lebanon, where eight of the nine television channels are affiliated with and funded by religious–political parties. It explores the existence of bias against atheists on televised Lebanese talk shows and news reports (2010–2022). Our findings reveal significant bias (69% overall and over 85% in speaker prominence bias), with channels that promote communal religious practice exhibiting the highest levels. Ultimately, our findings demonstrate that television, as the most influential Lebanese and Arabic mass medium, likely affects the public’s negative perceptions of Arabic-speaking atheists. Our findings reflect the decrease in objectivity in conflict-based media and such media’s poor understanding or intentional disregard for media’s crucial role in building a fair, democratic society.

Do brown people have brown thoughts? Richard Rodriguez’s philosophy of race, culture, and identity

Ever since the publication of Hunger of Memory in 1982, Richard Rodriguez has been read by scholars almost exclusively through the lens of identity politics. Twenty years later, in Brown, Rodriguez sought to reconcile some of the contradictions of identity, at least his own. Ultimately, Rodriguez concludes that the contradictions of identity cannot be reconciled. Our identities are always more complex and nuanced than the boxes we try to fit people in. In expressing his philosophy of identity, Rodriguez reflects on the concept of brownness and “brown thoughts,” invoking brown as a metaphor for cultural miscegenation, and thereby for diversity and genuine inclusion. Rodriguez’s philosophy of race, culture, and identity moves us beyond toxic forms of tribalism and cultural essentialism to an identity politics that unites rather than excluding and dividing.

Social world knowledge: Modeling and applications.

Social world knowledge is a key ingredient in effective communication and information processing by humans and machines alike. As of today, there exist many knowledge bases that represent factual world knowledge. Yet, there is no resource that is designed to capture social aspects of world knowledge. We believe that this work makes an important step towards the formulation and construction of such a resource. We introduce SocialVec, a general framework for eliciting low-dimensional entity embeddings from the social contexts in which they occur in social networks. In this framework, entities correspond to highly popular accounts which invoke general interest. We assume that entities that individual users tend to co-follow are socially related, and use this definition of social context to learn the entity embeddings. Similar to word embeddings which facilitate tasks that involve text semantics, we expect the learned social entity embeddings to benefit multiple tasks of social flavor. In this work, we elicited the social embeddings of roughly 200K entities from a sample of 1.3M Twitter users and the accounts that they follow. We employ and gauge the resulting embeddings on two tasks of social importance. First, we assess the political bias of news sources in terms of entity similarity in the social embedding space. Second, we predict the personal traits of individual Twitter users based on the social embeddings of entities that they follow. In both cases, we show advantageous or competitive performance using our approach compared with task-specific baselines. We further show that existing entity embedding schemes, which are fact-based, fail to capture social aspects of knowledge. We make the learned social entity embeddings available to the research community to support further exploration of social world knowledge and its applications.

Prefigurative pedagogies for working toward peace and justice in changing times: insights from Korea

ABSTRACT This study examines the contribution of university educators toward prefiguratively creating tomorrow today in the higher education classroom. Educators often teach for peace and social justice through a variety of normative pedagogical frameworks. Yet, this linkage of pedagogy and prefigurative politics in university classrooms is frequently under-examined. Moreover, such research has not yet been conducted within the context of Korea. Hence, this paper investigates – through a qualitative case study methodology – the varied approaches to pedagogy for social change in Korean universities today, including but not limited to: (a) democratic pedagogy, (b) critical pedagogy, (c) peace and social justice pedagogy, and (d) decolonial pedagogy. The paper examines these intersectional pedagogies through the lens of prefigurative politics, which is understood as a form of praxis that attempts to (re)construct through education a more just society. Findings indicate challenges and opportunities that Korean university educators face today in teaching for social change in higher education classrooms.