Media and Journalismo, vol. 26 N49 (2026)

Deadline: April 30, 2026

Editors:

• Berta García Orosa iD icon “ University of Santiago de Compostela, Spain, berta.garcia@usc.es
  
• Inês Amaral iD icon “ University of Coimbra, Portugal,  ines.amaral@uc.pt
  
• Noel Pascual Presa iD icon “ University of Santiago de Compostela, Spain, noel.pascual.presa@usc.es
  

The topic of this call for papers seeks to gather original, interdisciplinary, and empirically grounded research that exploreshow audiences are constructed within digital public spheres. The development of technologies such as artificial intelligence or big data has not only transformed the production, distribution, and circulation of information, but also redefined theways in which audiences are imagined and constructed. In its early stages (approximately 20 years ago), the continuous analysis of big data allowed for real-time audience insights and, subsequently, the prediction of audience behaviour, as exemplified by the Cambridge Analytica case. However, the focus has now shifted towards constructing audiences beforemessages are even produced, particularly in the context of electoral campaigns.

While there is a growing academic interest in the effects of media automation and personalisation, there has yet to be aconvergence of studies that systematically examine the epistemological, political, ethical, and communicative implicationsof this new relationship between algorithms and audiences. This gap is even more striking when considering the far-reaching nature of the phenomenon, which spans across journalism, political communication, digital culture, and platformgovernance.

In this fourth wave of digital communication, algorithms not only predict audience behaviours but also influence and shape them, giving rise to what has been termed the "algorithmic audience" (Riemer & Peter, 2021). This process ofdatafication has led to new methods of classification, personalisation, and micro-segmentation of audiences, profoundlytransforming the logic of political mediation.

This scenario marks a paradigm shift: while traditional scientific episteme conceived of audiences through ascribed categories such as class, gender, or ideology, the new algorithmic paradigm is grounded in behavioural data, adopting aperformative logic that dissolves fixed classifications (Fisher & Mehozay, 2019).

However, this transformation is far from neutral. The new ways of constructing algorithmic audiences present democraticrisks: automated biases (Kordzadeh & Ghasemaghaei, 2021), opacity in content selection (Livingstone, 2019), challengesto informational plurality and freedom of expression (Riemer & Peter, 2021), and growing inequality in voice representation (Jones, 2023; Zarouali et al., 2021). The construction of new public spheres requires critical and urgentanalysis.

These changes are affecting public discourse, with journalism at the forefront of the transformation. The growing relianceon algorithms is reshaping the profession, giving rise to what has been termed "automated journalism" or "robot journalism", driven by the automation and personalisation of news content (Carlson, 2015; Clerwall, 2014). Although thispersonalisation offers opportunities to strengthen the relationship with audiences (Ford & Hutchinson, 2019), it also introduces challenges, as public trust in the media may be undermined by the perceived risks inherent to these dynamics(Livingstone, 2019; Sehl & Eder, 2023). These new tools have far-reaching implications, both professionally and socially:from threats to freedom of expression and the need for new policies on content authorship, to the impact on the legitimacy of journalistic judgement and the reconfiguration of audiences (Carlson, 2018; Fisher & Mehozay, 2019; Montal & Reich,2016; Riemer & Peter, 2021).

From an identity perspective, the relationship with audiences remains central. However, the emphasis has shifted:personalised and individualised messaging have lost prominence, giving way to a more community-centred discourse. Inpractice, community is constructed around paid subscriptions and access to exclusive features and content. Narratives areconstructed around this group of members or subscribers to persuade them of their relevance to the survival and qualityof the media’s journalistic practice.

At the same time, users often perceive algorithmic content selection based on their consumption behaviour in a positive light (Thurman, 2018). This personalisation is accompanied by increasing categorisation and micro-segmentation, allowing for more granular and precise user classification (Beauvisage et al., 2024). Nonetheless, this positive perception and micro-segmentation do not protect users from the risks inherent to algorithmic governance, often carefully designedaround opaque or hidden interests (Jones, 2023; Reynolds & Hallinan, 2024).

This Call for Papers aims to:

• Explore epistemological transformations in the conceptualisation of audiences
  
• Analyse emerging journalistic and communicative practices within algorithmic logics
  
• Examine the democratic, ethical, and regulatory implications of algorithm-mediated personalisation
  
• Propose innovative methodologies for investigating hyper-segmented and opaque audiences
  
• Foster interdisciplinary dialogue bridging political communication, digital sociology, platform economics, and critical theory

Suggested topics for articles

• Political audiences and datafication
  
• Automated journalism and personalized news delivery
  
• Algorithmic biases and polarisation
  
• Algorithmic transparency and accountability
  
• Ideological segmentation and targeting strategies
  
• Civic participation in automated media environments
  
• Ethics, privacy, and data governance
  
• New forms of audience agency and performativity
  
• Youth audiences and platform culture
  
• Regional and comparative case studies
  
• Content automation
  
• Ethical and privacy implications of datafication
  
• The role of journalism in algorithmic communication
  
• Risks and opportunities of hyper-personalization
  
• Transformation of media consumption habits
  
• Informational plurality
  
• Echo chambers and information bubbles
  
• Polarisation and algorithmic bias
  
• Impact of algorithms on agenda setting
  
• Transformation of media power
  
• Trust in sources of algorithmic information
  
• Disinformation and fake news
  
• Transparency and regulatory mechanisms
  
• Audiences and engagement
  
• Media literacy
  
• New audiences and youth audiences
  
• Astroturfing campaigns
  

At the point of submission, the author must explicitly indicate the journal issue to which the manuscript is being submitted.

IMPORTANT DATES

Deadline for submitting articles: from January 22 to April 30, 2026

Publication period: continuous edition (September to December 2026) 

This call for papers is part of the R&D projects Artificial Intelligence in Digital Media in Spain: Effects and Roles (PID2024-156034OB-C22), funded by MICIU/AEI/10.13039/501100011033 and by “ERDF/EU”; & (d)e-HATE - Exploring Cyber Hate: Online Racism Targeting Immigrant and Racialized Communities in Portugal" (2024.18170.PEX).

Media & Jornalismo (RMJ) is a peer-reviewed scientific journal, indexed in Scopus and the Web of Science (EmergingSources Citation). Each paper is sent to two reviewers, who are invited in advance to evaluate it based on the criteria ofquality, originality, and relevance in line with the aim and theme of the specific issue of the journal.

Articles can be submitted in English, Spanish, and Portuguese.

Manuscripts must be submitted through the journal’s website (https://impactum-journals.uc.pt/mj). Once accessing RMJfor the first time, registration is required to submit the article and track the editorial process. We recommend reviewing the Author Guidelines, Submission Conditions, and thejournal's Editorial Policy.

For more information, you can contact patriciacontreiras@fcsh.unl.pt

References

Beauvisage, T., Beuscart, J.-S., Coavoux, S., & Mellet, K. (2024). How online advertising targets consumers: The uses of categories and algorithmic tools by audience planners. New Media & Society, 26(10), 6098-6119.https://doi.org/10.1177/14614448221146174

Carlson, M. (2018). Automating judgment? Algorithmic judgment, news knowledge, and journalistic professionalism. New Media & Society, 20(5), 1755-1772.https://doi.org/10.1177/1461444817706684

Carlson, M. (2015). "The Robotic Reporter: Automated Journalism and the Redefinition of Labor, Compositional Forms, and Journalistic Authority." Digital Journalism, 3(3), 416-431. https://doi.org/10.1080/21670811.2014.976412

Clerwall, C. (2014). "Enter the Robot Journalist: Users’Perceptions of Automated Content." Journalism Practice, 8(5), 519-531. https://doi.org/10.1080/17512786.2014.883116

Fisher, E., & Mehozay, Y. (2019). How algorithms see their audience: media epistemes and the changing conception of the individual. Media, Culture & Society, 41(8), 1176-1191. https://doi.org/10.1177/0163443719831598

Ford, H., & Hutchinson, J. (2019). Newsbots That Mediate Journalist and Audience Relationships. Digital Journalism, 7(8), 1013-1031. https://doi.org/10.1080/21670811.2019.1626752

Jones, C. (2023). How to train your algorithm: The struggle for public control over private audience commodities on Tiktok. Media, Culture & Society, 45(6), 1192-1209. https://doi.org/10.1177/01634437231159555

Kordzadeh, N., & Ghasemaghaei, M. (2021). Algorithmic bias: review, synthesis, and future research directions.European Journal of Information Systems, 31(3), 388-409. https://doi.org/10.1080/0960085X.2021.1927212

Livingstone, S. (2019). Audiences in an Age of Datafication: Critical Questions for Media Research. Television & New Media, 20(2), 170-183. https://doi.org/10.1177/1527476418811118

Montal, T., & Reich, Z. (2016). I, Robot. You, Journalist. Who is the Author? Authorship, bylines and full disclosure in automated journalism. Digital Journalism, 5(7), 829-849. https://doi.org/10.1080/21670811.2016.1209083

Reynolds, C., & Hallinan, B. (2024). User-generated accountability: Public participation in algorithmic governance onYouTube. New Media & Society, 26(9), 5107-5129. https://doi.org/10.1177/14614448241251791

Riemer, K., & Peter, S. (2021). Algorithmic audiencing: Why we need to rethink free speech on social media. Journal of Information Technology, 36(4), 409-426. https://doi.org/10.1177/02683962211013358

Sehl, A., & Eder, M. (2023). News Personalization and Public Service Media: The Audience Perspective in ThreeEuropean Countries. Journalism and Media, 4(1), 322-338. https://doi.org/10.3390/journalmedia4010022

Thurman, N., Moeller, J., Helberger, N., & Trilling, D. (2018). My Friends, Editors, Algorithms, and I: Examining audience attitudes to news selection. Digital Journalism, 7(4), 447-469. https://doi.org/10.1080/21670811.2018.1493936

Thurman, N. (2018). Social Media, Surveillance, and News Work: On the apps promising journalists a "crystal ball." Digital Journalism, 6(1), 76-97. https://doi.org/10.1080/21670811.2017.1345318

Zarouali, B., Helberger, N., & De Vreese, C. H. (2021). Investigating Algorithmic Misconceptions in a Media Context: Source of a New Digital Divide? Media and Communication, 9(4), 134-144. https://doi.org/10.17645/mac.v9i4.4090