PhD Scientific Days 2025

Mental Health Sciences I.

How Algorithms Shape Online Health Information-Seeking: A Case Study on Cow's Milk Protein Allergy in Hungarian Google Searches

Előadó neve

Ms. Kovácsné Hegedűs Dóra

Neptun code

UWIULG

Előadó munkahelye

Semmelweis University

Előadó telefonszáma

+36308340177

Előadó e-mail címe

hegedusdori26@gmail.com

Az előadás címe

How Algorithms Shape Online Health Information-Seeking: A Case Study on Cow's Milk Protein Allergy in Hungarian Google Searches

Szerző(k) neve és munkahelye

Kovácsné Hegedűs Dóra¹, Dr. Sztárayné Dr. Kézdy Éva², Lengyel Lívia¹, Dr. Hidvégi Edit Phd³, Prof. Dr. Albert Fruzsina¹, Susovits Kitti⁴

1: Semmelweis University
2: Károli Gáspár University of the Reformed Church in Hungary
3: Semmelweis University Clinic of Pulmonology
4: .

Bemutatás módja

Szóbeli

Szekció

Mental Health Sciences I.

Language of the presentation

Hungarian

Preferred session

Mental Health Sciences

Összefoglaló szövege

Introduction:
Search engines not only organize but also shape how users think about health. While digital health research often relies on English-language data, the influence of search algorithms in smaller linguistic communities is understudied. This study focuses on Hungarian-language searches related to cow's milk protein allergy (CMPA)—a misunderstood condition often confused with lactose and milk protein intolerance.
Aims:
We aim to examine how algorithmic tools such as Google Ads and Google Autocomplete influence health information-seeking behavior regarding CMPA in Hungary. The study explores thematic patterns, search volume trends, and the emergence of misinformation through digital recommendation systems.
Methods:
We conducted a mixed-methods infodemiological analysis using quantitative data from Google Ads Keyword Planner (2020–2023) and qualitative data from Google Autocomplete. Keyword suggestions for "milk allergy", "milk protein allergy," and "cow's milk protein allergy" were categorized and analyzed. Quantitative analysis was performed using Stata 18, while qualitative coding was conducted in Atlas.ti using thematic content analysis.
Results:
CMPA-related search volumes increased annually, with seasonal peaks in January. Among all keyword suggestions, 37% addressed symptoms (especially dermatological), 25% concerned nutrition, and 24% reflected terminological confusion. Although diagnostic terms were less frequent, interest in such queries grew steadily. Autocomplete suggested non-evidence-based tests (e.g., IgG tests) over professional guidelines. Algorithmic prioritization often favored commercial over medical content. Infant-related searches were the most dominant, suggesting parents were engaging in proxy-seeking behavior.
Conclusion:
Search engines do more than guide users—they shape what users find. Our findings highlight how algorithmic design influences online health information, leading to conceptual confusion and potential overdiagnosis, especially among concerned parents. This study demonstrates how the Nobeco Effect operates within a lesser-studied language space, revealing risks and opportunities in digital health literacy.
Funding:
This research was conducted without external funding.

University

Semmelweis University

Supervisor

Prof. Dr. Albert Fruzsina, Dr. Sztárayné Dr. Kézdy Éva Rita

Publication of my abstract

I give consent to the publication of my abstract on the website of the congress.

phd.section.field

in doctoral studies before complex exam (PhD)

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

jóváhagyta

Előadó

9052

Start

16:15

End

16:30