From bias to opinion

Guerra, Paula; Veloso, Adriano; Meira, Wagner; Almeida, Virgílio A. F.

doi:10.1145/2020408.2020438

Cited by 83 publications

(4 citation statements)

References 26 publications

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“…First, polarization is relevant from the sociological point of view, because it often leads to segregation and conflits in the society (DiMaggio et al 1996). Secondly, it plays an important role in opinion analysis and similar tasks (Calais et al 2011). In particular, it may shed more light on polarized debates and help to predict their outcomes (Walton 1991).…”

Section: Introductionmentioning

confidence: 99%

The Interplay Between Conformity and Anticonformity and its Polarizing Effect on Society

Siedlecki

Szwabiński

Weron

2016

JASSS

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Abstract:Simmering debates leading to polarization are observed in many domains. Although empirical findings show a strong correlation between this phenomenon and modularity of a social network, still little is known about the actual mechanisms driving communities to conflicting opinions. In this paper, we used an agentbased model to check if the polarization may be induced by a competition between two types of social response: conformity and anticonformity. The proposed model builds on the q-voter model (Castellano et al. b) and uses a double-clique topology in order to capture segmentation of a community. Our results indicate that the interplay between intra-clique conformity and inter-clique anticonformity may indeed lead to a bi-polarized state of the entire system. We have found a dynamic phase transition controlled by the fraction L of negative cross-links between cliques. In the regime of small values of L the system is able to reach the total positive consensus. If the values of L are large enough, anticonformity takes over and the system always ends up in a polarized stated. Putting it the other way around, the segmentation of the network is not a su icient condition for the polarization to appear. A suitable level of antagonistic interactions between segments is required to arrive at a polarized steady state within our model.

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Section: Introductionmentioning

confidence: 99%

The Interplay Between Conformity and Anticonformity and its Polarizing Effect on Society

Siedlecki

Szwabiński

Weron

2016

JASSS

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“…For labels, methods to mitigate crowd worker biases are proposed: leveraging psychology and social computing theory [184] for political social media content; resolving disagreement in mined resources such as data from social media [64], or review ratings of items [109]; disambiguating biases from the task design [51,88]; and allocating crowd workers based on their demographics [17].…”

Section: Biased Annotationsmentioning

confidence: 99%

Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems

2021

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The increasing use of data-driven decision support systems in industry and governments is accompanied by the discovery of a plethora of bias and unfairness issues in the outputs of these systems. Multiple computer science communities, and especially machine learning, have started to tackle this problem, often developing algorithmic solutions to mitigate biases to obtain fairer outputs. However, one of the core underlying causes for unfairness is bias in training data which is not fully covered by such approaches. Especially, bias in data is not yet a central topic in data engineering and management research. We survey research on bias and unfairness in several computer science domains, distinguishing between data management publications and other domains. This covers the creation of fairness metrics, fairness identification, and mitigation methods, software engineering approaches and biases in crowdsourcing activities. We identify relevant research gaps and show which data management activities could be repurposed to handle biases and which ones might reinforce such biases. In the second part, we argue for a novel data-centered approach overcoming the limitations of current algorithmic-centered methods. This approach focuses on eliciting and enforcing fairness requirements and constraints on data that systems are trained, validated, and used on. We argue for the need to extend database management systems to handle such constraints and mitigation methods. We discuss the associated future research directions regarding algorithms, formalization, modelling, users, and systems.

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“…Researchers use sentiment analysis for detecting not only biased news but also comments made by news readers [45]. The problem inherent to sentiment analysis is that labeled data is not readily available to train classifiers [13] for many tasks. Individuals are affected by different kinds of biased content [25].…”

Section: Media Bias With External Knowledgementioning

confidence: 99%

Untitled

2018

Trans. Soc. Comput.

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It has been observed that different media outlets exert bias in the way they report the news, which seamlessly influences the way that readers' knowledge is built through filtering what we read. Therefore, understanding bias in news media is fundamental for obtaining a holistic view of a news story. Traditional work has focused on biases in terms of "agenda setting," where more attention is allocated to stories that fit their biased narrative. The corresponding method is straightforward, since the bias can be detected through counting the occurrences of different stories/themes within the documents. However, these methods are not applicable to biases which are implicit in wording, namely, "framing" bias. According to framing theory, biased communicators will select and emphasize certain facts and interpretations over others when telling their story. By focusing on facts and interpretations that conform to their bias, they can tell the story in a way that suits their narrative. Automatic detection of framing bias is challenging since nuances in the wording can change the interpretation of the story. In this work, we aim to investigate how the subtle pattern hidden in language use of a news agency can be discovered and further leveraged to detect frames. In particular, we aim to identify the type and polarity of frame in a sentence. Extensive experiments are conducted on real-world data from different countries. A case study is further provided to reveal possible applications of the proposed method. CCS Concepts: • Human-centered computing → Collaborative and social computing; • Information systems → Web mining;

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From bias to opinion

Cited by 83 publications

References 26 publications

The Interplay Between Conformity and Anticonformity and its Polarizing Effect on Society

The Interplay Between Conformity and Anticonformity and its Polarizing Effect on Society

Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems

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