Setting the Agenda: The Mass Media and Public Opinion

(1) Whereas the objectives of the Community, as laid down in the Treaty, as amended by the Treaty on European Union, include creating an ever closer union among the peoples of Europe, fostering closer relations between the States belonging to the Community, ensuring economic and social progress by common action to eliminate the barriers which divide Europe, encouraging the constant improvement of the living conditions of its peoples, preserving and strengthening peace and liberty and promoting democracy on the basis of the fundamental rights recognized in the constitution and laws of the Member States and in the European Convention for the Protection of Human Rights and Fundamental Freedoms;

Some Preliminary Comments on the DIRECTIVE 95/46/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data.

Authored by Dr. Safiya U. Noble, an assistant professor at the University of Southern California Annenberg School of Communication, this text is one of the preeminent works that explicitly addresse...

Algorithms of Oppression: How Search Engines Reinforce Racism

We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process. We further find that these papers' proposed quantitative techniques for measuring or mitigating "bias" are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing "bias" in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of "bias"---i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements---and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities.

Language (Technology) is Power: A Critical Survey of "Bias" in NLP

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

TikTok is a video-sharing social networking service, whose popularity is increasing rapidly. It was the world's second-most downloaded app in 2019. Although the platform is known for having users posting videos of themselves dancing, lip-syncing, or showcasing other talents, user-videos expressing political views have seen a recent spurt. This study aims to perform a primary evaluation of political communication on TikTok. We collect a set of US partisan Republican and Democratic videos to investigate how users communicated with each other about political issues. With the help of computer vision, natural language processing, and statistical tools, we illustrate that political communication on TikTok is much more interactive in comparison to other social media platforms, with users combining multiple information channels to spread their messages. We show that political communication takes place in the form of communication trees since users generate branches of responses to existing content. In terms of user demographics, we find that users belonging to both the US parties are young and behave similarly on the platform. However, Republican users generated more political content and their videos received more responses; on the other hand, Democratic users engaged significantly more in cross-partisan discussions.

Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok

Word embeddings are a widely used set of natural language processing techniques that map words to vectors of real numbers. These vectors are used to improve the quality of generative and predictive models. Recent studies demonstrate that word embeddings contain and amplify biases present in data, such as stereotypes and prejudice. In this study, we provide a complete overview of bias in word embeddings. We develop a new technique for bias detection for gendered languages and use it to compare bias in embeddings trained on Wikipedia and on political social media data. We investigate bias diffusion and prove that existing biases are transferred to further machine learning models. We test two techniques for bias mitigation and show that the generally proposed methodology for debiasing models at the embeddings level is insufficient. Finally, we employ biased word embeddings and illustrate that they can be used for the detection of similar biases in new data. Given that word embeddings are widely used by commercial companies, we discuss the challenges and required actions towards fair algorithmic implementations and applications.

https://dl.acm.org/doi/pdf/10.1145/3351095.3372843

Bias in word embeddings

Political communication on social media: A tale of hyperactive users and bias in recommender systems

We present a simple NLP methodology for detecting COVID-19 misinformation videos on YouTube by leveraging user comments. We use transfer learning pre-trained models to generate a multi-label classifier that can categorize conspiratorial content. We use the percentage of misinformation comments on each video as a new feature for video classification.

/pdf/nlp-based-feature-extraction-for-the-detection-of-covid-19-2d272wlltd.pdf

NLP-based Feature Extraction for the Detection of COVID-19 Misinformation Videos on YouTube

Amongst other methods, political campaigns employ microtargeting, a specific technique used to address the individual voter. In the US, microtargeting relies on a broad set of collected data about ...

https://journals.sagepub.com/doi/pdf/10.1177/2053951718811844

Juan Carlos Medina Serrano

Papers

Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok

Bias in word embeddings

Political communication on social media: A tale of hyperactive users and bias in recommender systems

NLP-based Feature Extraction for the Detection of COVID-19 Misinformation Videos on YouTube

Social media and microtargeting: Political data processing and the consequences for Germany: