Examples of tweets texts (Portuguese).

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.

本研究的核心目标为详述2022年巴西总统大选期间（含选举后阶段及2023年1月行政、立法、司法三机构大楼遇袭事件）从Twitter（现X平台）提取数据的采集流程。本次数据采集工作耗时一年。此外，本研究还对由2.82亿条推文构建而成的数据集"The Interfaces Twitter Elections Dataset"（简称ITED-Br）的总体特征进行了概述，该数据集是全球第三大规模的政治性推文数据集。本次研究的数据采集与建库流程共分为三大阶段，下设多个子环节：（1）平台基础特性与运行机制初探；（2）语境分析、概念模型构建与关键词界定；（3）数据采集策略落地实施。研究开发了适配各类主要采集场景的Python算法，其中“令牌农场（token farm）”算法用于循环调用可用API密钥。尽管Twitter（现X平台）本质属于公开访问平台且符合大数据标准，但由于数据体量庞大、流转速度快且异构性强，从中提取有价值的信息并非易事。本研究最终得出结论：要获取具备信息价值的内容，不仅需要掌握社会政治领域的专业知识，还需具备计算与信息研究相关技能，凸显了此类研究的跨学科属性。