Citizens get informed, on a daily basis, from social networks in general and from the media in particular. Accordingly, the media are increasingly expressing their concern about phenomena related to disinformation. This article presents an analysis of the social networks of 159 Chilean media that, over 5 years, referred to fake news or disinformation on 10,699 occasions. Based on data science strategies, the Queltehue platform was programmed to systematically track the information posted by 159 media on their social networks (Instagram, Facebook and Twitter). The universe of data obtained (13 million news items) was filtered with a specific query to reach 10,699 relevant posts, which underwent textual computer analysis (LDA) complemented with manual strategies of multimodal discourse analysis (MDA). Among the findings, it is revealed that the recurrent themes over the years have mostly referred to fake news and politics and fake news related to health issues. This is widely explained on the grounds of a political period in Chile which involved at least five electoral processes, in addition to the global COVID-19 pandemic. Regarding the multimodal analysis, it is observed that when the dissemination of fake news involves well-known figures such as politicians or government authorities, an image or a video in which such figure appears is used. In these cases, two phenomena occur: (a) these figures have the opportunity to rectify their false or misinforming statements or (b) in most cases, their statements are reiterated and end up reinforcing the controversy. In view of these results, it seems necessary to ask whether this is all that can be done and whether this is enough that communication can do to guarantee healthy and democratic societies.

Wikipedia plays a crucial role in the integrity of the Web. This work analyzes the reliability of this global encyclopedia through the lens of its references. We operationalize the notion of reference quality by defining reference need (RN), i.e., the percentage of sentences missing a citation, and reference risk (RR), i.e., the proportion of non-authoritative references. We release Citation Detective, a tool for automatically calculating the RN score, and discover that the RN score has dropped by 20 percent point in the last decade, with more than half of verifiable statements now accompanying references. The RR score has remained below 1% over the years as a result of the efforts of the community to eliminate unreliable references. We propose pairing novice and experienced editors on the same Wikipedia article as a strategy to enhance reference quality. Our quasi-experiment indicates that such a co-editing experience can result in a lasting advantage in identifying unreliable sources in future edits. As Wikipedia is frequently used as the ground truth for numerous Web applications, our findings and suggestions on its reliability can have a far-reaching impact. We discuss the possibility of other Web services adopting Wiki-style user collaboration to eliminate unreliable content.

The success of Wikipedia and other user-generated content communities has been driven by the openness of recruiting volunteers globally, but this openness has also led to a persistent lack of trust in its content. Despite several attempts at developing trust indicators to help readers more quickly and accurately assess the quality of content, challenges remain for practical deployment to general consumers. In this work we identify and address three key challenges: empirically determining which metrics from prior and existing community approaches most impact reader trust; 2) validating indicator placements and designs that are both compact yet noticed by readers; and 3) demonstrating that such indicators can not only lower trust but also increase perceived trust in the system when appropriate. By addressing these, we aim to provide a foundation for future tools that can practically increase trust in user generated content and the sociotechnical systems that generate and maintain them.

Information presented in Wikipedia articles must be attributable to reliable published sources in the form of references. This study examines over 5 million Wikipedia articles to assess the reliability of references in multiple language editions. We quantify the cross-lingual patterns of the perennial sources list, a collection of reliability labels for web domains identified and collaboratively agreed upon by Wikipedia editors. We discover that some sources (or web domains) deemed untrustworthy in one language (i.e., English) continue to appear in articles in other languages. This trend is especially evident with sources tailored for smaller communities. Furthermore, non-authoritative sources found in the English version of a page tend to persist in other language versions of that page. We finally present a case study on the Chinese, Russian, and Swedish Wikipedias to demonstrate a discrepancy in reference reliability across cultures. Our finding highlights future challenges in coordinating global knowledge on source reliability.

This research has two main goals: i) to compare how Chilean and Spanish news outlets discuss fake news, and ii) to propose a mixed methodology, combining data science and content analysis methods in order to understand how this journalistic treatment is done. To build the corpus analyzed on this study, we used a crawler that followed Chilean and Spanish news outlets in Twitter, and on top of that large dataset (over than 2 million tweets per year, per country), we searched for a set of keywords appearing on the headings or snippets of the news linked to those tweets. We obtained 663 pieces of news, and editorial columns from the Chilean data, and 466 pieces of news from the Spanish data coming from 50 news sources, from January 2017 to December 2019. From our analysis, we concluded that the journalistic treatment is similar in both countries in several dimensions. However, the Spanish news sources make more efforts in trying to educate the audience to identify fake news.