Fake news and copyright

As is known, new technologies have profoundly changed the way content is produced, shared and disseminated. One of the most recent (and worrying) changes is the phenomenon of ‘fake news’, especially since disinformation and intentional misrepresentation of real information have started to affect individual decision-making in the political sphere. It is a worrying phenomenon because the dissemination of fake news can challenge democratic values and undermine national security. Against this background, can copyright play a role in the fight against fake news? And what is the relationship between such news and copyright in the first place? Fake news in theory falls within copyright subject matter and may often meet the requirements for protection. The paper analyses three recent examples of fake news which have been widely disseminated online – and makes the point that copyright may subsist in such news. Yet, despite such content being potentially capable of attracting protection, we propose to remove any copyright which may arise on grounds of public interest. Indeed, when a work is protected by copyright, right holders have an incentive to exploit it, as the monopoly granted to them increases the ability to extract profits out of the work, for example via licensing. This may contribute to encouraging creators of fake news to spread such content across multiple channels to reach wide audiences. Excluding copyright could therefore help make fake news less appealing. A short reference will also be made to copyright defences which may be relied on by entities and individuals who check news’ accuracy (fact-checkers) – that is, the fair use doctrine under US law and several exceptions under EU (and UK) law, namely transient use, text and data mining, criticism and review and public security. * All authors contributed equally to this manuscript and are listed alphabetically.

Mapping the Sensory Fingerprint of Swedish Beer Market through Text and Data Mining and Multivariate Strategies

The continuous increase of online data with consumers’ and experts’ reviews and preferences is a potential tool for sensory characterization. The present work aims to overview the Swedish beer market and understand the sensory fingerprint of Swedish beers based on text data extracted from the Swedish alcohol retail monopoly (Systembolaget) website. Different multivariate strategies such as heatmaps, correspondence analysis and hierarchical cluster analysis were used to understand the sensory space of the different beer styles. Additionally, sensory space for specific hop cultivars was also investigated. Results highlighted Gothenburg as the main producing area in Sweden. The style Indian Pale Ale (IPA) is the largest available at the retail monopoly. From a sensory perspective, commonalities and differences were found between beer types and styles. Based on the aroma description, different types of ale and lager can cluster together (such as Porter and Stout and Dark lagers). Additionally, an associative relationship between specific aromas and hop cultivars from text data information was successfully achieved.

Text and data mining of in-copyright works

How copyright law might be an impediment to text and data mining research.

Methods of Mining User-Generated Online Substance Use Data: A Scoping Review (Preprint)

BACKGROUND Social media and online discussion forums offer a unique data source for medical and public health research. Using these platforms, people who use drugs often discuss valuable information including adverse effects, formulations, and reasons for use. OBJECTIVE Since this data is often unstructured, text and data mining methods are required to extract and analyze these posts systematically. This scoping review summarizes the literature on text and data mining methods for online substance use content. METHODS Online databases including PubMed and EMBASE were searched to identify articles meeting the eligibility criteria. Titles and abstracts were first screened by two reviewers and any conflicts were resolved with discussion. Data extraction was performed by two reviewers using an identical template to record information. Any disagreements were resolved with discussion. RESULTS The search identified 1131 articles, 26 of which were included for data extraction. Most articles presented unique data mining methods. The five most common strategies included sentiment analysis, topic modeling, data classification, clustering, and association learning. CONCLUSIONS Data mining offers a valuable avenue for retrieving useful information from online discussion forums to supplement conventional data sources in medical and public health research. With respect to substance use content, association learning and regression analysis were particularly well-suited for analyzing this data. Future research should focus on confirming the validity and reliability of these data mining methods, while establishing links between data mining, clinical evaluation, and knowledge translation.

Article 2—Definitions

This chapter provides the definition of terms covered in Article 2 of Directive 2019/790 regarding copyright in the Digital Single Market in Europe. It begins with the term 'research organisation', which means a university, research institute, or any other entity that conduct scientific research or carry out educational activities involving the conduct of scientific research. It also explains text and data mining, which is an automated analytical technique that analyses text and data in digital form in order to generate information about patterns, trends, and correlations. The chapter defines cultural heritage institution as a publicly accessible library or museum, an archive, or a film or audio heritage institution, while press publication means a collection of literary works of a journalistic nature. It describes the tasks of an online content-sharing service provider, such as providing information society service that store and give public access to a large amount of copyright-protected works or other protected subject matter uploaded by its users.

Article 3—Text and Data Mining for the Purposes of Scientific Research

This chapter focuses on the laws about text and data mining for scientific researchstipulated under Article 3 of the Directive 2019/790 or copyright directive of the Digital Single Market in Europe. It examines the legislation that require Member States to provide an exception for reproductions and extractions made by research organisations and cultural heritage institutions on text and data mining of works or other subject matter for the purposes of scientific research. It also stresses that copies of works or other subject matter on text and data mining will be stored with an appropriate level of security and retained for the purposes of scientific research. The chapter talks about rightholders, which are allowed to apply measures to ensure the security and integrity of the networks and databases. It mentions Member States that encourage rightholders, research organisations, and cultural heritage institutions to define commonly agreed best practices concerning the application of the obligation and measures on text and data mining.

Article 4—Exception or Limitation for Text and Data Mining

This chapter focuses on Article 4 of Directive 2019/790, the European copyright directive, which require Member States to provide for an exception or limitation for reproductions and extractions of works and other subject matter for the purposes of text and data mining. It talks about digital technologies that permit new types of uses that are not clearly covered by the existing Union rules on exceptions and limitations in the fields of research, innovation, education, and preservation of cultural heritage. It also describes the optional nature of exceptions and limitations that could negatively impact the functioning of the internal market. The chapter discusses the exceptions and limitations provided in Directive 2019/790 that seek to achieve a fair balance between the rights and interests of authors, other rightholders, and users. It clarifies that text and data mining can be carried out in relation to mere facts or data that are not protected by copyright.

Text and Data Mining Exception in South America: A Way to Foster AI Development in the Region

In 2015, authors wondered if Europe was falling behind in the artificial intelligence (AI) race because of the lack of a text and data mining (TDM) exception. What can then be said for South America? Copyright regimes and their interaction with the development of digital technologies in this continent have been overlooked by authors. This paper intends to start filling this gap by mapping the current state of copyright exceptions that serve computational analysis in South America. After reviewing the copyright regimes of the five largest economies of the region (i.e. Argentina, Brazil, Chile, Colombia and Peru), I concluded that they are not prepared for digital research techniques such as text and data mining. Researchers in these countries are at a competitive disadvantage, as rigid and outdated copyright regimes act as a constraint against keeping pace with the latest developments in subsequent years. If policymakers want to develop their nations’ AI capabilities, as many governments and international organizations claim they do, they will need to look for a more flexible and enabling approach to copyright.