Personalized News Recommendation with Context Trees

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We propose an interpretable model to score the subjective bias present in documents, based only on their textual content. Our model is trained on pairs of revisions of the same Wikipedia article, where one version is more biased than the other. Although pr ...

The drastic shift towards digital communication in our mediasphere has caused a profound change in the production and consumption of information, which in turn has substantial implications on the social and political landscape. Misinformation, as a side ef ...

Understanding how high-quality newspapers present and discuss major news plays a role towards tackling disinformation, as it contributes to the characterization of the full ecosystem in which information circulates. In this paper, we present an analysis of ...

In the era of misinformation and information inflation, the credibility assessment of the produced news is of the essence. However, fact-checking can be challenging considering the limited references presented in the news. This challenge can be transcended ...

We demonstrate the SciLens News Platform, a novel system for evaluating the quality of news articles. The SciLens News Platform automatically collects contextual information about news articles in real-time and provides quality indicators about their valid ...

This paper describes, develops, and validates SciLens, a method to evaluate the quality of scientific news articles. The starting point for our work are structured methodologies that define a series of quality aspects for manually evaluating news. Based on ...

While spreading fake news is an old phenomenon, today social media enables misinformation to instantaneously reach millions of people. Content-based approaches to detect fake news, typically based on automatic text checking, are limited. It is indeed diffi ...

Personalization is pervasive in the online space as it leads to higher efficiency for the user and higher revenue for the platform by individualizing the most relevant content for each user. However, recent studies suggest that such personalization can lea ...

Our modern society is struggling with an unprecedented amount of online misinformation, which does harm to democracy, economics, and cybersecurity. Journalism and politics have been impacted by misinformation on a global scale, with weakened public trust i ...

The proliferation and rapid diffusion of fake news on the Internet highlight the need of automatic hoax detection systems. In the context of social networks, machine learning (ML) methods can be used for this purpose. Fake news detection strategies are tra ...

Online social data has been hailed to provide unprecedented insights into human phenomena due to its ability to capture human behavior at a scale and level of detail, both in breadth and depth, that is hard to achieve through conventional data collection t ...

Social media is becoming more and more integrated in the distribution and consumption of news. How is news in social media different from mainstream news? % This paper presents a comparative analysis covering a span of 17 months and hundreds of news events ...

Early modern printed gazettes relied on a system of news exchange and text reuse largely based on handwritten sources. The reconstruction of this information exchange system is possible by detecting reused texts. We present a method to individuate text bor ...

With an ever-growing amount of data generated on the web, aggregating information from the crowd into meaningful knowledge has become crucial to companies in order to create a competitive edge. Not only companies, but also organizations and governments can ...

We present the Personalized News (PEN) recommender systems framework, currently in use by a newspaper website to evaluate various algorithms for news recommendations. We briefly describe its system architecture and related components. We show how a researc ...

We present the Personalized News (PEN) recommender systems framework, (Note that this paper is an extended abstract of Garcin2013b), currently in use by a newspaper website to evaluate various algorithms for news recommendations. We briefly describe its sy ...

Search engines essentially rely on the structure of the graph of hyperlinks. Although accurate for the main trend, this is not effective when some query is ambiguous. Leveraging semantic information by the mean of interest matching allows proposing complem ...

We present WhatsUp, a collaborative filtering system for disseminating news items in a large-scale dynamic setting with no central authority. WhatsUp constructs an implicit social network based on user profiles that express the opinions of users about the ...

Because of the abundance of news on the web, news recommendation is an important problem. We compare three approaches for personalized news recommendation: collaborative filtering at the level of news items, content-based system recommending items with sim ...

This paper reports on the successful use of Graasp, a social media platform, by university students for their collaborative work. Graasp features a number of innovations, such as administrator-free creation of collaborative spaces, context-aware recommenda ...

Personalized News Recommendation with Context Trees

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It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons

Combating Online Scientific Misinformation

How Did Europe's Press Cover Covid-19 Vaccination News? A Five-Country Analysis

On Representation Learning for Scientific News Articles Using Heterogeneous Knowledge Graphs

SciLens News Platform: A System for Real-Time Evaluation of News Articles

SciLens: Evaluating the Quality of Scientific News Articles Using Social Media and Scientific Literature Indicators

The Fake News Vaccine

A dashboard for controlling polarization in personalization

Debunking Misinformation on the Web: Detection, Validation, and Visualisation

Automatic Online Fake News Detection Combining Content and Social Signals

Probing the Limits of Social Data : Biases, Methods, and Domain Knowledge

Comparing Events Coverage in Online News and Social Media: The Case of Climate Change

Mapping the Early Modern News Flow: An Enquiry by Robust Text Reuse Detection

Aggregating information from the crowd : ratings, recommendations and predictions

PEN recsys: a Personalized News Recommender Systems Framework

PEN recsys: a Personalized News Recommender Systems Framework (DEMO)

CoFeed: privacy-preserving Web search recommendation based on collaborative aggregation of interest feedback

WHATSUP: A Decentralized Instant News Recommender

Personalized news recommendation based on collaborative filtering

A Social Media Platform in Higher Education