Algorithms without frontiers? How language-based algorithmic information disparities for suicide crisis information sustain digital divides over time in 17 countries

ABSTRACT

This study focuses on the changes in the global digital divide produced by language-based, algorithmic information disparities in relation to crisis-prevention resources for suicide available through the Google search engine. We used agent-based testing to emulate Google searches performed in 17 countries and in 16 different languages as a direct replication and extension of previous work. We compare data collected in 2017 with data collected in 2021. Our analyses revealed that Google searches in English from within the United States still have the highest likelihood of triggering the display of additional crisis-prevention information prominently shown in addition to the regular search results (i.e., Google’s suicide-prevention result). Searches in Spanish from within the United States are informationally disadvantaged. Display rates are only slightly lower in other English-speaking countries and when searches are performed in English. While information disparities and digital divides narrowed between 2017 and 2021, substantial differences in the display of crisis-prevention resources remain observable within multilingual countries, especially when other languages compete with English. In Bahrain, South Africa, and Sweden, the crisis-prevention information functionality seems unimplemented. Our findings suggest that the use of automated computational methods is both useful to continuously observe the implementation of new algorithmic functionalities and necessary to hold global media institutions accountable for their actions.

KEYWORDS:

• Digital divide
• Google algorithm
• algorithmic information disparities
• language-based information disparities
• comparative computational methods

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All data is available open access from their OSF repository under https://osf.io/hnmte/.

Notes

1 As a control condition, we also included a list of search terms that were unrelated to suicide. The SPR never showed up and showed up nowhere in response to any of the control terms (which is not explicitly mentioned in the results).

Additional information

Funding

There has been no financial support that could have influenced the outcome.

Notes on contributors

Sebastian Scherr

Sebastian Scherr (PhD, University of Munich) is an Assistant Professor of Health Communication at the Department of Communication at Texas A&M University, USA. His research interests focus on individual and structural susceptibility factors for media effects in the domains of health and political communication, with a special emphasis on mental health, suicide prevention, and empirical methods.

Florian Arendt

Florian Arendt (PhD, University of Vienna) holds the Tenure Track Professorship in Health Communication at the Department of Communication, University of Vienna, Austria. His research focuses on health communication with a special emphasis on suicide prevention.

Mario Haim

Mario Haim (PhD, University of Munich) is a Full Professor of Communication Science, especially Computational Communication Research, at the Department of Media and Communication at LMU Munich, Germany. His main research interests are computational journalism, news use within algorithmically curated media environments, and computational social science.