activity
20222026
most citedPanning for gold: Lessons learned from the platform-agnostic automated detection of political content in textual data

5 citations · 13 across the 8 of their papers we have counts for

collaborators

12 papers

cs.CL2026

SWARM: A Multilingual Human-Annotated Dataset for Russian Propaganda Detection in Search Engine Results

Manuel Tonneau, Abhinav Dubey, Farhan Shaikh +8

Russian state propaganda spreads across many languages and online spaces. Yet, most computational work examines only one such space, usually social media, in one or two languages,…

cs.CY2026

High-Risk Memories? Comparative audit of the representation of Second World War atrocities in Ukraine by generative AI applications

Mykola Makhortykh, Victoria Vziatysheva, Maryna Sydorova

The rise of generative artificial intelligence (genAI) models poses new possibilities and risks for how the past is remembered by accelerating content production and altering the p…

cs.CY2025

Invisible in Search? Auditing Aesthetic Bias in the Visual Representation of Holocaust Victims on Google

Mykola Makhortykh, Tobias Rohrbach, Maryna Sydorova

Information retrieval systems, such as search engines, increasingly shape the representation of the past and present states of social reality. Despite their importance, these syste…

cs.CY2025

Campaigning through the lens of Google: A large-scale algorithm audit of Google searches in the run-up to the Swiss Federal Elections 2023

Tobias Rohrbach, Mykola Makhortykh, Maryna Sydorova

Search engines like Google have become major sources of information for voters during election campaigns. To assess potential biases across candidates' gender and partisan identiti…

cs.CY2025

Search engines in polarized media environment: Auditing political information curation on Google and Bing prior to 2024 US elections

Mykola Makhortykh, Tobias Rorhbach, Maryna Sydorova +1

Search engines play an important role in the context of modern elections. By curating information in response to user queries, search engines influence how individuals are informed…

cs.HC2024

Google, How Should I Vote? How Users Formulate Search Queries to Find Political Information on Search Engines

Victoria Vziatysheva, Mykola Makhortykh, Maryna Sydorova +1

Search engine results depend not only on the algorithms but also on how users interact with them. However, factors affecting the selection of a search query remain understudied. Us…

cs.CY2024

LLMs as information warriors? Auditing how LLM-powered chatbots tackle disinformation about Russia's war in Ukraine

Mykola Makhortykh, Ani Baghumyan, Victoria Vziatysheva +2

The rise of large language models (LLMs) has a significant impact on information warfare. By facilitating the production of content related to disinformation and propaganda campaig…

cs.CL2024★ 1 cited

Finding frames with BERT: A transformer-based approach to generic news frame detection

Vihang Jumle, Mykola Makhortykh, Maryna Sydorova +1

Framing is among the most extensively used concepts in the field of communication science. The availability of digital data offers new possibilities for studying how specific aspec…

cs.CY2024★ 2 cited

Finding the white male: The prevalence and consequences of algorithmic gender and race bias in political Google searches

Tobias Rohrbach, Mykola Makhortykh, Maryna Sydorova

Search engines like Google have become major information gatekeepers that use artificial intelligence (AI) to determine who and what voters find when searching for political inform…

cs.IR2024

Algorithmically Curated Lies: How Search Engines Handle Misinformation about US Biolabs in Ukraine

Elizaveta Kuznetsova, Mykola Makhortykh, Maryna Sydorova +3

The growing volume of online content prompts the need for adopting algorithmic systems of information curation. These systems range from web search engines to recommender systems a…

cs.CY2023★ 5 cited

Examining bias perpetuation in academic search engines: an algorithm audit of Google and Semantic Scholar

Celina Kacperski, Mona Bielig, Mykola Makhortykh +2

Researchers rely on academic Web search engines to find scientific sources, but search engine mechanisms may selectively present content that aligns with biases embedded in queries…

cs.CL2022★ 5 cited

Panning for gold: Lessons learned from the platform-agnostic automated detection of political content in textual data

Mykola Makhortykh, Ernesto de León, Aleksandra Urman +5

The growing availability of data about online information behaviour enables new possibilities for political communication research. However, the volume and variety of these data ma…