Evidence map›Paper›PMID 39899510›Full record

ArticlePloS one2025

The Interfaces Twitter Elections Dataset: Construction process and characteristics of big social data during the 2022 presidential elections in Brazil.

Sylvia Iasulaitis, Alan Demétrius Baria Valejo, Bruno Cardoso Greco, Vinicius Gonçalves Perillo, Guilherme Henrique Messias, Isabella Vicari, with the Interfaces—Center for Sociopolitical Studies of Algorithms and Artificial Intelligence

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Sylvia IasulaitisDepartment of Social Sciences, Federal University of São Carlos, São Paulo, Brazil.ORCID https://orcid.org/0000-0002-3526-1003
Alan Demétrius Baria ValejoDepartment of Computing, Federal University of São Carlos, São Paulo, Brazil.
Bruno Cardoso GrecoDepartment of Information Science, Federal University of São Carlos, São Paulo, Brazil.ORCID https://orcid.org/0009-0005-2263-851X
Vinicius Gonçalves PerilloDepartment of Computing, Federal University of São Carlos, São Paulo, Brazil.
Guilherme Henrique MessiasGraduate Program in Computing Science, Federal University of São Carlos, São Paulo, Brazil.
Isabella VicariGraduate Program in Science, Technology, and Society, Federal University of São Carlos, São Paulo, Brazil.ORCID https://orcid.org/0000-0002-1095-7346
with the Interfaces—Center for Sociopolitical Studies of Algorithms and Artificial Intelligence

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The main objective of this study is to describe the process of collecting data extracted from Twitter (X) during the Brazilian presidential elections in 2022, encompassing the post-election period and the event of the attack on the buildings of the executive, legislative, and judiciary branches in January 2023. The work of collecting data took one year. Additionally, the study provides an overview of the general characteristics of the dataset created from 282 million tweets, named "The Interfaces Twitter Elections Dataset" (ITED-Br), the third most extensive dataset of tweets with political purposes. The process of collecting and creating the database for this study went through three major stages, subdivided into several processes: (1) A preliminary analysis of the platform and its operation; (2) Contextual analysis, creation of the conceptual model, and definition of Keywords and (3) Implementation of the Data Collection Strategy. Python algorithms were developed to model each primary collection type. The "token farm" algorithm, was employed to iterate over available API keys. While Twitter is generally a "public" access platform and fits into big data standards, extracting valuable information is not trivial due to the volume, speed, and heterogeneity of data. This study concludes that acquiring informational value requires expertise not only in sociopolitical areas but also in computational and informational studies, highlighting the interdisciplinary nature of such research.

Indexed as

Big DataPoliticsSocial MediaAlgorithmsBrazilHumans

Identifiers

PMID39899510
PMCPMC11790155

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.