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arXiv 2104.00124cs.CLcs.SI
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卢甘达语-英语混合语码社交媒体文本中的错误信息检测

Misinformation detection in Luganda-English code-mixed social media text

Makerere University(马凯雷雷大学)

Peter Nabende, David Kabiito, Claire Babirye, Hewitt Tusiime, Joyce Nakatumba-Nabende

摘要:

社交媒体平台上错误信息的发生、形式和负面影响越来越多,需要更多的错误信息检测工具。目前,正在努力解决COVID-19的错误信息,但是,对于40种不同的乌干达土著语言中的任何一种,都没有错误信息检测工具。本文通过介绍基本的语言资源和错误信息检测数据集来解决这一差距,该数据集基于来自Facebook和Twitter社交媒体平台的代码混合的卢甘达语-英语消息。几种机器学习方法应用于错误信息检测数据集,以开发用于检测代码混合的卢甘达-英语消息是否包含错误信息的分类模型。在一个实验性的错误信息检测任务中,对分类方法进行了10倍交叉验证评估,结果表明,判别式多项朴素贝叶斯(DMNB)方法分别达到了最高的准确率和78.19%和77.90%的F-测度。此外,支持向量机和Bagging集成分类模型取得了可比的结果。这些结果是有希望的,因为机器学习模型基于仅来自错误信息检测数据集的n元特征。

英文摘要:

The increasing occurrence, forms, and negative effects of misinformation on social media platforms has necessitated more misinformation detection tools. Currently, work is being done addressing COVID-19 misinformation however, there are no misinformation detection tools for any of the 40 distinct indigenous Ugandan languages. This paper addresses this gap by presenting basic language resources and a misinformation detection data set based on code-mixed Luganda-English messages sourced from the Facebook and Twitter social media platforms. Several machine learning methods are applied on the misinformation detection data set to develop classification models for detecting whether a code-mixed Luganda-English message contains misinformation or not. A 10-fold cross validation evaluation of the classification methods in an experimental misinformation detection task shows that a Discriminative Multinomial Naive Bayes (DMNB) method achieves the highest accuracy and F-measure of 78.19% and 77.90% respectively. Also, Support Vector Machine and Bagging ensemble classification models achieve comparable results. These results are promising since the machine learning models are based on n-gram features from only the misinformation detection dataset.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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