Elements for Automatic Identification of Fallacies in Mexican Election Campaign Political Speeches

IF 0.7 4区计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Programming and Computer Software Pub Date : 2024-01-24 DOI:10.1134/s0361768823080170

Kenia Nieto-Benitez, Noe Alejandro Castro-Sanchez, Hector Jimenez Salazar, Gemma Bel-Enguix, Dante Mújica Vargas, Juan Gabriel González Serna, Nimrod González Franco

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引用次数: 0

Abstract

Political speeches frequently use fallacies to sway voters during electoral campaigns. This study presents an approach for implementing machine learning models to automatically identify a specific type of fallacy known as an “appeal to emotion” fallacy. The objective is to establish a set of elements that enable the application of fallacy mining, as in existing literature, fallacies are typically identified manually, and there is no established structure for applying mining techniques. Our method utilizes features derived from an emotion lexicon to differentiate between valid arguments and fallacies, and we employed Support Vector Machine and Multilayer Perceptron models. Our results indicate that the Multilayer Perceptron model achieved an F1‑score of 0.60 in identifying fallacies. Based on our analysis, we recommend the use of lexical dictionaries to effectively identify “appeal to emotion” fallacies.

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自动识别墨西哥竞选政治演讲谬误的要素

摘要在竞选期间，政治演讲经常使用谬误来左右选民。本研究提出了一种实施机器学习模型的方法，用于自动识别一种特定类型的谬误，即 "诉诸情感 "谬误。我们的目标是建立一套能够应用谬误挖掘的要素，因为在现有文献中，谬误通常是由人工识别的，而且没有应用挖掘技术的既定结构。我们的方法利用从情感词典中提取的特征来区分有效论据和谬误，并采用了支持向量机和多层感知器模型。结果表明，多层感知器模型在识别谬误方面的 F1 分数为 0.60。根据我们的分析，我们建议使用词汇词典来有效识别 "诉诸情感 "谬误。

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来源期刊

Programming and Computer Software 工程技术-计算机：软件工程

CiteScore

1.60

自引率

28.60%

发文量

审稿时长

>12 weeks

期刊介绍： Programming and Computer Software is a peer reviewed journal devoted to problems in all areas of computer science: operating systems, compiler technology, software engineering, artificial intelligence, etc.