Influence on democracy

As noted in the introduction, the literature indicates that the widespread use of algorithms greatly influences the functioning of democratic political systems, and that bots’ influence also continues to grow (Montal & Reich, 2017; Santini et al., 2018), especially during campaign season. Research on this topic focuses on a technical definition of algorithms and primarily seeks to develop detection systems through machine learning (Häring et al., 2018; Dubois & McKelvey, 2019). Meanwhile, in the social sciences, researchers question the health of democracy due to the spread of fake news (Bimber & Gil-de-Zúñiga, 2020), as well as astroturfing campaigns that can manipulate and sow uncertainty (Zerback et al., 2021).

Today, doubt has been cast on some concepts that were otherwise widely accepted in recent years, such as bubble filters (Puschmann, 2019). Some studies indicate that social media reinforce existing attitudes (Ohme, 2021). Others discuss social media’s influence on the public sphere (Kaluža, 2021). There also are studies that question the validity of the term. Haramba et al. (2018) propose a historical interpretation from the perspective of the commodification of readership (García-Orosa, 2018). The goal of satisfying readers' habits, even with false, misleading, or biased information, stems from the attention economy and is a fundamental principle of algorithms that satisfies users by captivating them (Seaver, 2018). In this sense, Schjøtt-Hansen and Hartley (2021) analyze algorithms and news selections to describe the evolution from distributing news content to readers/viewers treated as segments of consumer groups to algorithmically constructing individual readers/viewers as aggregate data points.

After a decade of euphoria about platforms’ potential to empower citizens, disinformation, fake news, incitement to hatred and the Cambridge Analytica scandal, among others, have engendered mistrust (van-Dijck, 2020). Scientific literature highlights the risks in algorithms’ potential to slander and the defenselessness of the media and citizens, as pointed out by Lewis et al. (2019). The authors draw attention to two relevant issues: the difficulty in finding guilty parties in defamation cases and in finding defenses as powerful as those wielded by the platforms.

These influences on democracy have led some authors to speak of "algorithmic culture" (Gilbert, 2018) and potential threats to democratic values. In recent years, scholars have called for legislative reforms to address the new challenges that online communication poses for democratic values or specific issues such as legislation on bots (Jones & Jones, 2019), privacy and facial recognition (Leong, 2019), or incidences of racism caused by algorithms (Turner-Lee, 2018).

Journalism and media

State-of-the-art technology has affected the practice of journalism in recent years (López-García & Vizoso, 2021), and the use of algorithms has sparked debates about the industry’s core definition and foundation. Researchers have coined different names for the use of algorithms, (Vállez & Codina, 2018) among which automated, algorithmic, or robot journalism are the most used. Under this label, scholars have analyzed, fundamentally from the perspective of journalists and media directors themselves, the consequences of the implementation and use of algorithms in the production, distribution, and circulation of information.

In recent years, a growing number of media outlets, such as The Associated Press, The Washington Post, and the BBC, have embraced "automated journalism," (Graefe, 2018), also known as "algorithmic journalism" (Kotenidis & Veglis, 2021) or “robot journalism” (Waddell, 2018), understood as the automatic generation of journalistic texts through software and algorithms, with little or no human intervention, except for the initial programming (Danzon-Chambaud & Cornia, 2021; Sehl et al., 2021). Nonetheless, algorithms also intervene in the phase of selecting the issue, sources, and circulation of the journalistic message. Automation is studied from the perspective of helping journalists, for example, in the search for newsworthy events (Diakopoulos et al., 2021; Thurman et al., 2017), in personalized distribution by news recommendation systems (Helberger, 2019), in promoting data journalism (Tong & Zuo, 2021), in evaluating the credibility of sources (Fletcher et al., 2020; Graefe et al., 2018), or in redefining news values (Choi, 2019).

Overall, the results reveal the transformative role of machines, especially in the news-gathering and distribution phases, and increasingly in the writing phase, especially in data-rich specialties such as sports and economics. However, journalists continue to control all phases of the news production process (Milosavljević & Vobič, 2019), especially in the news selection and editing phases, suggesting a desire to protect their role as final arbiters of meaning (Wu et al., 2019). Several authors have studied the potential for the robotization of journalism (Borges & Gambarato, 2019; Dierickx, 2021), and some have concluded that robots do not threaten their work (De-la-Torre, 2020).

As changes in the profession come to light, a significant re-working of the logic of journalism is leading to a new conceptualization of the field and technology’s influence on it. The studies examine automation as one element of journalists’ work (Calvo-Rubio & Ufarte-Ruiz, 2020) and identify contradictions between automation and some of the fundamental ideals of journalism, like public service, autonomy and objectivity (Milosavljević & Vobič, 2019), which leads to friction when implemented in newsrooms (Hermida & Young, 2017). Journalists point to the nature of the sources and robots’ lack of a “nose for news” as some of the limitations of automated journalism (Thurman et al., 2017).

After the period of 2015 to 2016, which was partly characterized by a very favorable and uncritical attitude, the most recently published texts (2017-2019) once again opted for the neutral tone typical of 2011 and 2012 (Parratt-Fernández et al., 2021). Research has emerged that questions the role of journalism within society and the consequences of algorithmizing the profession, as well as social, economic, political, and cultural life, and attempts to re-imagine the field (Bucher, 2017) and analyze challenges centered on ethics (Dörr & Hollnbuchner, 2017) and credibility (Tandoc et al., 2020). Such studies describe algorithmic journalism and new challenges in the fight against the dominance of commercial interests (more visible in outlets’ business departments than in newsrooms) in the implementation of automation (Slaček-Brlek et al., 2017).

Other authors highlight the growing dependence on software providers and platforms in the face of editorial independence, which should prevail in journalism (Schapals & Porlezza, 2020; Weber & Kosterich, 2018), due to the role of professionals, such as technologists, i.e., computer scientists or “technoactors,” new to the (Canavilhas et al., 2016) production processes, who influence the news and redefine journalism with their practices and values (Wu et al., 2019).

Audience

Automation has sparked new debates on the production of journalistic texts and their authorship (Montal & Reich, 2017), and in some situations it is no longer possible to determine who produces the news (Wölker & Powell, 2021). Moreover, automation has also changed journalists’ relationships with the audience, for example, through the use of newsbots as mediators between journalist and audience (Ford & Hutchinson, 2019). Since the beginning of online journalism, the audience has been part of the journalist's work (García-Orosa, 2018), but the use of algorithms is a step forward that has two implications. First, through audience monitoring, “[...] journalists can—and do—monitor social network users and their content via sophisticated, professional apps that are also used by police and security forces. (Thurman, 2018: 1). Secondly, journalists can create algorithmic audiences in line with the interests of news outlets. Martin (2021) warns of the risks of the mediatization of news visibility through opaque algorithms, as well as through the platformization of news (van-Dijck et al., 2018) and the metrification of news values.

Algorithms not only influence what content is featured; the audience is also ranked according to their interest in the platform. Regarding Facebook, Thorson et al. (2021) suggest that people who are algorithmically categorized as interested in news or politics are more likely to attract content to their feeds, regardless of their self-reported interest in civic content. In this sense, Papakyriakopoulos et al. (2018) discuss the relevance of hyperactive users (users with above-average activity on the network) in shaping public opinion and democracy. The authors study their influence, which affects public opinion on social networks, and warn of the possible adverse consequences of algorithms and recommendation systems for political systems. Therefore, one of the most important aspects is circulation. Media outlets have gone from disseminating content to audiences and managing their activities, to transforming the audience into constructors of the discourse and creating algorithmic audiences based on previously-obtained big data. Bodó (2019) describes how European media, instead of focusing on increasing user engagement in the short term, try to personalize the news to increase audience loyalty in the long term. “Unlike the ‘platform logic of personalization’, which uses personalization to produce engagement and sell audiences to advertisers, they have developed a “news logic of personalization” that uses personalization to sell news to audiences. (Bodó, 2019: 1054).

New social, political, and media roles are conquering spaces as algorithms, a generalized trope to give meaning to social life (Ziewitz, 2017), which not only shapes the agenda, but also constructs the audience (Thorson et al., 2021). The media seek an audience that is "constructed" rather than a naturally arising one (Eldridge et al., 2019). As such, algorithmic audiences are programmed (Møller-Hartley et al., 2021) to promote a “particularly populist ‘profitable and normal’ media experience” (Harper, 2017). Users are often defenseless because they are unaware of how news are filtered and prioritized (Powers, 2017) and how the user profile is predicted.

Algorithmic determination of consumption

Literature highlights recommendation systems as shapers of public opinion and, therefore, of civic participation in public life. The massive consumption of information on social media platforms, which has dealt a blow to traditional media, has led to a significant dependence on the algorithmic determination of news consumption based on previous audience behavior, analyzed through big data, and possible distortions such as polarization (Shmargad & Klar, 2020). News personalization systems are viewed as black boxes that indicate a significant disconnection between the practice and theory of algorithmic transparency, particularly in non-community contexts (Bastian et al., 2021). The use of different data sources to predict what content will be interesting to readers raises concerns about possible audience fragmentation (Makhortykh & Wijermars, 2021); after tracking news personalization for six years and detecting platform commodification, Kunert and Thurman (2019) also raised concerns about data protection and the effects of recommendation systems.

But there are also traditional media projects that use news recommendation systems transparently to combat disinformation and create a European public sphere, which seems to be confirmed by an analysis of the news they have produced (Canavilhas, 2022). Such is the case of the European Broadcasting Union’s “A European Perspective,” whose PEACH ecosystem seeks to offer the most appropriate content to each user at the most opportune time and on the most appropriate device. The system highlighted by recent academic literature sparks an important debate on the opacity of recommendation and content adaptation systems and, therefore, on their role in democratic systems (Helberger, 2019).