Political Ideology and Polarization of Policy Positions: A Multi-dimensional Approach

Sinno, Barea; Oviedo, Bernardo; Atwell, Katherine; Alikhani, Malihe; Li, Junyi Jessy

doi:10.48550/arxiv.2106.14387

Cited by 3 publications

(5 citation statements)

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“…“ Close reading ,” i.e., a thoughtful interpretation of texts, was explicitly mentioned as the method used by Bonikowski et al [ 76 ] and Sinno et al [ 69 ], for example. In the latter work, close reading was used to understand the motivations behind annotators' decisions.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, Grover et al [30] carried out a qualitative analysis of tweets containing terms that were found to be important by LIWC analysis. "Close reading," i.e., a thoughtful interpretation of texts, was explicitly mentioned as the method used by Bonikowski et al [76] and Sinno et al [69], for example. In the latter work, close reading was used to understand the motivations behind annotators' decisions.…”

Section: The Use Of Qualitative Methodsmentioning

confidence: 99%

“…According to Tsur et al [53], topic co-occurrence can also approximate the way topics are framed. In other cases, see e.g., Sinno et al [69], topic modeling was used only for technical reason to create a topically coherent corpus by omitting articles that were not related to the relevant topics.…”

Section: Topic Modelingmentioning

confidence: 99%

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A scoping review on the use of natural language processing in research on political polarization: trends and research prospects

Németh

2022

J Comput Soc Sc

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As part of the “text-as-data” movement, Natural Language Processing (NLP) provides a computational way to examine political polarization. We conducted a methodological scoping review of studies published since 2010 (n = 154) to clarify how NLP research has conceptualized and measured political polarization, and to characterize the degree of integration of the two different research paradigms that meet in this research area. We identified biases toward US context (59%), Twitter data (43%) and machine learning approach (33%). Research covers different layers of the political public sphere (politicians, experts, media, or the lay public), however, very few studies involved more than one layer. Results indicate that only a few studies made use of domain knowledge and a high proportion of the studies were not interdisciplinary. Those studies that made efforts to interpret the results demonstrated that the characteristics of political texts depend not only on the political position of their authors, but also on other often-overlooked factors. Ignoring these factors may lead to overly optimistic performance measures. Also, spurious results may be obtained when causal relations are inferred from textual data. Our paper provides arguments for the integration of explanatory and predictive modeling paradigms, and for a more interdisciplinary approach to polarization research.

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Section: Resultsmentioning

confidence: 99%

Section: The Use Of Qualitative Methodsmentioning

confidence: 99%

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A scoping review on the use of natural language processing in research on political polarization: trends and research prospects

Németh

2022

J Comput Soc Sc

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“…Many datasets have been designed to study political bias at different granularity levels: news sources [22], [23], articles [24], paragraphs [25], sentences [26], [27], and headlines [6] with one or more bias types [28].…”

Section: A Datasetsmentioning

confidence: 99%

An Inferential Commonsense-Driven Framework for Predicting Political Bias in News Headlines

2023

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Identifying political bias in news headlines holds significant importance as it influences the dissemination and consumption of news stories. However, employing conventional methods to do so poses a formidable challenge, as the short headline text is often complex and lacks sufficient syntactic and semantic context. Existing approaches fail to acknowledge the potential of commonsense reasoning in facilitating text comprehension, although it has been shown to aid numerous downstream applications. To this end, to facilitate comprehension and compensate for the lack of context, we propose leveraging inferential commonsense knowledge to simplify, interpret, and explain events that are not explicitly stated in headlines. Furthermore, to fully utilise its potential and deal with the unnecessary noise it may introduce, we present a method for emphasising significant inferences. Using this knowledge, we introduce a novel framework, IC-BAIT, short for Inferential Commonsense aware BiAs IdenTifier, which is a flexible neural network framework designed to enhance political bias prediction in news headlines. We also present two bias-annotated datasets: MediaBias and GoodNews. Experiments on both datasets demonstrate that IC-BAIT significantly enhances the performance of the baseline models used in the framework. Experiments on the datasets show that IC-BAIT improves the baseline models in terms of accuracy (2.0-10.0%), macro-averaged F 1 (2.2-22.2%), Jaccard-score (up to 15.1%), and micro-averaged F 1 (up to 18.6%). Our in-depth qualitative analysis reveals the scenarios in which the selected knowledge is beneficial and when it is detrimental. Datasets and scripts are available at https://github.com/Swati17293/IC-BAIT.

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“…But, according to CrowdTangle (a Facebook's social media monitoring platform), Ad Fontes Media Bias Chart is increasing in popularity and spreading across the internet[14]. Its ratings are also used in reputable academic research such as[10,35,53,57,66]. More details on Ad Fontes' rating can be found in Appendix A.…”

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confidence: 99%

It's All Relative! A Method to Counter Human Bias in Crowdsourced Stance Detection of News Articles

Haq

Hui

2022

Proc. ACM Hum.-Comput. Interact.

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Using human intelligence to identify news articles' political stances is common in research and practical applications. But human judgement can be biased and prone to errors stemming from the comprehension of tasks and political alignment. This paper proposes a relative rating method based on news articles' stances relative to raters' own stances to avoid comprehension inconsistency and to control for human bias in crowdsourced stance detection of news articles. We also show how to use the relative ratings to construct a measure for raters' stances on a political topic and to identify raters whose ratings are of higher quality than others. We implement our proposed methods in an online experiment that recruits Amazon Mechanical Turk users as raters for news articles on Gun Control. Using the data from the experiment, we find evidence that raters' own stances on Gun Control significantly impact ratings of related news articles, both at the individual levels and at the aggregate levels. We also present evidence that our relative-rating-based stance measure captures more information about raters' actual stances than their self-reported stance does.

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Political Ideology and Polarization of Policy Positions: A Multi-dimensional Approach

Cited by 3 publications

References 0 publications

A scoping review on the use of natural language processing in research on political polarization: trends and research prospects

A scoping review on the use of natural language processing in research on political polarization: trends and research prospects

An Inferential Commonsense-Driven Framework for Predicting Political Bias in News Headlines

It's All Relative! A Method to Counter Human Bias in Crowdsourced Stance Detection of News Articles

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