scholarly journals Codes of conduct for algorithmic news recommendation: The Yandex.News controversy in Russia

First Monday ◽  
2021 ◽  
Author(s):  
Françoise Daucé ◽  
Benjamin Loveluck
Keyword(s):  
Quantitative Analysis ◽  
Public Sphere ◽  
Codes Of Conduct ◽  
Recommendation Systems ◽  
Electoral Fraud ◽  
Digital Platforms ◽  
Political Bias ◽  
Ongoing Effort ◽  
Ukrainian Crisis ◽  
News Recommendation

In Russia, since 2011, the Yandex.News aggregator (Yandex.Novosti) — the Russian equivalent to Google News — has been suspected of political bias in the context of protests against electoral fraud followed by the Ukrainian crisis. This article first outlines the issues associated with automated news recommendation systems, their role as “algorithmic gatekeepers” and the questions they raise in terms of news diversity and possible manipulation. It then analyses the controversies which have developed around Yandex.News, particularly since the authorities have decided to regulate the way it operates through a law adopted in 2016. Finally, it provides an audit of Yandex.News aggregation in 2020, through a quantitative analysis of its database of sources and of the Top 5 results presented on the Yandex homepage. It shows the discrepancy between the diversity of the Russian online mediasphere and the narrowness of the Yandex.News media sample. This research contributes to the sociology of digital platforms and the study of “governance by algorithms”, showing how the Yandex news aggregator is a key asset in the Russian government’s overall disciplining of the country’s media and digital public sphere, in an ongoing effort to assert “digital sovereignty”.

Remembering hope: mediated queer futurity and counterpublics in Turkey’s authoritarian times

2021 ◽  
pp. 1-23
Author(s):  
Yener Bayramoğlu
Keyword(s):  
Public Sphere ◽  
Authoritarian Regimes ◽  
Media Discourse ◽  
Digital Platforms ◽  
Coup D'etat ◽  
The Public ◽  
Textual Material ◽  
The Public Sphere ◽  
Tabloid Press ◽  
Looking Back

Abstract This article explores how hope and visions of the future have left their mark on media discourse in Turkey. Looking back at some of the events that took place in the 1980s, a decade that was shaped by the aftermath of the 1980 coup d’état, and considering them alongside what has happened since the ban of Istanbul’s Pride march in 2015, it examines traces of hope in two periods of recent Turkish history characterized by authoritarianism. Drawing on an array of visual and textual material drawn from the tabloid press, magazines, newspapers, and digital platforms, it inquires into how queer hope manages to infiltrate mediated publics even in times of pessimism and hopelessness. Based upon analysis of an archive of discourses on resistance, solidarity, and future, it argues that queer hope not only helps to map out possible future routes for queer lives in (and beyond) Turkey, but also operates as a driving political force that sustains queers’ determination to maintain their presence in the public sphere despite repressive nationalist, militarist, Islamist, and authoritarian regimes.

Linguistic markers of sexism in the Italian media: a case study of ministra and ministro

Corpora ◽  
2016 ◽  
Vol 11 (3) ◽  
pp. 371-399 ◽  
Author(s):  
Federica Formato
Keyword(s):  
Quantitative Analysis ◽  
Public Sphere ◽  
Media Use ◽  
Specific Language ◽  
Language Reform ◽  
Linguistic Markers ◽  
Job Titles ◽  
Sexist Language ◽  
Gender Specific

This paper examines the way that the Italian media use language to refer to female ministers in the last three governments. While Italian is a gender-specific language (e.g., a root of the job titles can be followed by either feminine or masculine morphemes, singular and plural), it is common to use masculine forms to refer to and address women. Ministro is one of those cases where masculine forms replace feminine ones – a practice which could be construed as sexist, is only rarely challenged in institutions, and to which attention has only recently been paid in academia ( Fusco, 2012 ; and Robustelli, 2012a , 2012b ). The investigation presented here focusses on how grammar is translated in a way that reproduces women's invisibility in a sexist society. A corpus-based quantitative analysis of feminine and masculine forms of ministr– used in three widely read printed Italian newspapers (Corriere della Sera, Il Resto del Carlino and La Stampa) is undertaken. Newspaper articles were collected in the period 2012–14 to cover the Monti technocratic government (three female ministers), and left-winged Letta (seven female ministers) and part of the Renzi (seven female ministers) political governments. This paper contributes to the literature on language reform and sexist language in traditionally male-inhabited physical and metaphysical (stereotypes, prototypes) spaces such as the institutional public sphere.

News Recommendation for China Sina Weibo Microblog Service Based on User Social Behaviors

2014 ◽  
pp. 220-243
Author(s):  
Zuo Yuchu ◽  
You Fang ◽  
Wang Jianmin ◽  
Zhou Zhengle
Keyword(s):  
Social Network ◽  
Quantitative Analysis ◽  
Recommendation System ◽  
Heuristic Rules ◽  
Network Service ◽  
Social Network Service ◽  
Ranking List ◽  
Sina Weibo ◽  
Definition Of ◽  
News Recommendation

Sina weibo microblog is an increasingly popular social network service in China. In this work, the authors conducted a study of detecting news in Sina weibo microblog. They found the traditional definition for news can be generalized here. They first expanded the definition of news by conducting user surveys and quantitative analysis. The authors built a news recommendation system by modeling the users, classifying them into four different groups, and applying several heuristic rules, which derived from the generalized definition of news. By applying the new recommendation system, people got newsworthy information, while the funny and interesting tweets, which are popular in Sina weibo microblog, were put in the last ranking list. This study helps us achieve better understanding of heuristic rules about news. Some official organizations can also benefit from the work by supervising the most popular news around civilians.

The Politics of Immersive Storytelling

2020 ◽  
pp. 175-190
Author(s):  
Christian Stiegler
Keyword(s):  
Social Media ◽  
Virtual Reality ◽  
Mass Media ◽  
Public Sphere ◽  
20Th Century ◽  
21St Century ◽  
Digital Platforms ◽  
The Public ◽  
Media Logic ◽  
The Public Sphere

This article applies and extends the concept of social media logic to assess the politics of immersive storytelling on digital platforms. These politics are considered in the light of what has been identified as mass media logic, which argues that mass media in the 20th century gained power by developing a commanding discourse that guides the organization of the public sphere. The shift to social media logic in the 21st century, with its grounding principles of programmability, popularity, connectivity, and datafication, influenced a new discourse on the logics of digital ecosystems. Digital platforms such as Facebook are offering all-surrounding mediated environments to communicate in Virtual Reality (‘Facebook Spaces') as well as immersive narratives such as Mr. Robot VR. This article provides an understanding of the politics of immersive storytelling and of its underlying principles of programmability, user experience, popularity, and platform sociality, which define immersive technologies in the 21st century.

scholarly journals News Recommendation Systems - Accomplishments, Challenges & Future Directions

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 16702-16725 ◽  
Author(s):  
Chong Feng ◽  
Muzammil Khan ◽  
Arif Ur Rahman ◽  
Arshad Ahmad
Keyword(s):  
Recommendation Systems ◽  
Future Directions ◽  
News Recommendation

scholarly journals Research on Recommendation Systems using Deep Learning Models

2019 ◽  
Vol 8 (4) ◽  
pp. 10544-10551
Keyword(s):  
Deep Learning ◽  
Recommender Systems ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Learning Models ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Future Preference ◽  
Movie Recommendation ◽  
News Recommendation

Recommender System is the effective tools that are accomplished of recommending the future preference of a set of products to the consumer and to predict the most likelihood items. Today, customers has the ability to purchase or sell different items with advancement of e-commerce website, nevertheless it made complicate to investigate the majority of appropriate items suitable for the interest of the consumer from many items. Due to this scenario, recommender systems that can recommend items appropriate for user's interest and likings have become mandatory. In recent days, various recommendation methods are applied to resolve the data abundance setback in numerous application areas like movie recommendation, e-commerce, news recommendation, song recommendation and social media. Even if all the available current recommender systems are successful in generating reasonable predictions, these recommendation system still facing the issues like accuracy, cold-start, sparsity and scalability problem. Deep learning, the recently developed sub domain of machine learning technique is utilized in recommendation systems to enhance the feature of predicted output. Deep Learning is used to generate recommendations and the research challenges specific to recommendation systems when using Deep Learning are also presented. In this research, the basic terminologies, the fundamental concepts of Recommendation engine and a wide-ranging review of deep learning models utilized in Recommender Systems are presented.

PNTRS

2022 ◽  
Vol 24 (3) ◽  
pp. 1-19
Author(s):  
Sunita Tiwari ◽  
Sushil Kumar ◽  
Vikas Jethwani ◽  
Deepak Kumar ◽  
Vyoma Dadhich
Keyword(s):  
Fuzzy Inference System ◽  
Recommendation System ◽  
Naive Bayes ◽  
Fuzzy Inference ◽  
Recommendation Systems ◽  
User Feedback ◽  
Satisfaction Level ◽  
Inference System ◽  
News Recommendation ◽  
Feedback Rating

A news recommendation system not only must recommend the latest, trending and personalized news to the users but also give opportunity to know about the people’s opinion on trending news. Most of the existing news recommendation systems focus on recommending news articles based on user-specific tweets. In contrast to these recommendation systems, the proposed Personalized News and Tweet Recommendation System (PNTRS) recommends tweets based on the recommended article. It firstly generates news recommendation based on user’s interest and twitter profile using the Multinomial Naïve Bayes (MNB) classifier. Further, the system uses these recommended articles to recommend various trending tweets using fuzzy inference system. Additionally, feedback-based learning is applied to improve the efficiency of the proposed recommendation system. The user feedback rating is taken to evaluate the satisfaction level and it is 7.9 on the scale of 10.

scholarly journals News Recommendation Systems in the Era of Information Overload

2021 ◽  
Author(s):  
Shuaishuai Feng ◽  
Junyan Meng ◽  
Jiaxing Zhang
Keyword(s):  
Information Overload ◽  
Recommendation Systems ◽  
Mobile Internet ◽  
Modern World ◽  
The Internet ◽  
The Public ◽  
Recommendation Algorithms ◽  
Cold Starts ◽  
News Recommendation ◽  
Source Of Information

The internet has reconstructed information boundaries in the modern world, and along with mobile internet has become the most important source of information for the public. Simultaneously, the internet has brought humanity into an era of information overload. In response to this information overload, recommendation systems backed by big data and smart algorithms have become highly popular on information platforms on the internet. There have already been many studies that attempted to improve and upgrade recommendation algorithms from a technical perspective, but the field lacks a comprehensive reflection on news recommendation algorithms. In our study, we summarize the principles and characteristics of current news recommendation algorithms and discuss “unexpected consequences” that might arise from these algorithms. In particular, technical bottlenecks include cold starts and data sparsity, and moral bottlenecks are presented in the form of information imbalance and manipulation. These problems may cause new recommendation systems to become a “warped mirror”.

scholarly journals Algorithmic accountability

2018 ◽  
Vol 376 (2128) ◽  
pp. 20170362 ◽  
Author(s):  
Hetan Shah
Keyword(s):  
Machine Learning ◽  
Public Sphere ◽  
Public Interest ◽  
Social Impact ◽  
Codes Of Conduct ◽  
Ethics Training ◽  
The Public ◽  
Algorithmic Accountability ◽  
Licence To Operate ◽  
The Public Sphere

There is enormous opportunity for positive social impact from the rise of algorithms and machine learning. But this requires a licence to operate from the public, based on trustworthiness. There are a range of concerns relating to how algorithms might be held to account in areas affecting the public sphere. This paper outlines a number of approaches including greater transparency, monitoring of outcomes and improved governance. It makes a case that public sector bodies that hold datasets should be more confident in negotiating terms with the private sector. It also argues that all regulators (not just data regulators) need to wake up to the challenges posed by changing technology. Other improvements include diversity of the workforce, ethics training, codes of conduct for data scientists, and new deliberative bodies. Even if these narrower issues are solved, the paper poses some wider concerns including data monopolies, the challenge to democracy, public participation and maintaining the public interest.This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations’.

Introduction

2021 ◽  
pp. 1-16
Keyword(s):  
Public Sphere ◽  
Social Networking ◽  
Digital Technologies ◽  
The State ◽  
Digital Platforms ◽  
Nationalist Movements ◽  
The Public ◽  
Modern Politics ◽  
The Public Sphere

This chapter introduces readers to concepts of nationalism in historic and modern contexts. The concept of Otherness and alienation that are a symptom and result of increased nationalism in modern politics is explored. The use of digital technologies, including social networking platforms by contemporary nationalist movements is highlighted, explaining the influence that is afforded to average citizens via these tools. Communicative digital platforms are enabling dissent against both society and the state, and the egalitarian nature of digital technologies allows for the swift and often effective mobilization of protest that transcends the online into the public sphere.

Sign in / Sign up

Export Citation Format

Share Document