Detection of Hate Speech in COVID-19-Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach

Abstract

Background: The massive scale of social media platforms requires an automatic solution for detecting hate speech. These automatic solutions will help reduce the need for manual analysis of content. Most previous literature has cast the hate speech detection problem as a supervised text classification task using classical machine learning methods or, more recently, deep learning methods. However, work investigating this problem in Arabic cyberspace is still limited compared to the published work on English text.

Objective: This study aims to identify hate speech related to the COVID-19 pandemic posted by Twitter users in the Arab region and to discover the main issues discussed in tweets containing hate speech.

Methods: We used the ArCOV-19 dataset, an ongoing collection of Arabic tweets related to COVID-19, starting from January 27, 2020. Tweets were analyzed for hate speech using a pretrained convolutional neural network (CNN) model; each tweet was given a score between 0 and 1, with 1 being the most hateful text. We also used nonnegative matrix factorization to discover the main issues and topics discussed in hate tweets.

Results: The analysis of hate speech in Twitter data in the Arab region identified that the number of non-hate tweets greatly exceeded the number of hate tweets, where the percentage of hate tweets among COVID-19 related tweets was 3.2% (11,743/547,554). The analysis also revealed that the majority of hate tweets (8385/11,743, 71.4%) contained a low level of hate based on the score provided by the CNN. This study identified Saudi Arabia as the Arab country from which the most COVID-19 hate tweets originated during the pandemic. Furthermore, we showed that the largest number of hate tweets appeared during the time period of March 1-30, 2020, representing 51.9% of all hate tweets (6095/11,743). Contrary to what was anticipated, in the Arab region, it was found that the spread of COVID-19-related hate speech on Twitter was weakly related with the dissemination of the pandemic based on the Pearson correlation coefficient (r=0.1982, P=.50). The study also identified the commonly discussed topics in hate tweets during the pandemic. Analysis of the 7 extracted topics showed that 6 of the 7 identified topics were related to hate speech against China and Iran. Arab users also discussed topics related to political conflicts in the Arab region during the COVID-19 pandemic.

Conclusions: The COVID-19 pandemic poses serious public health challenges to nations worldwide. During the COVID-19 pandemic, frequent use of social media can contribute to the spread of hate speech. Hate speech on the web can have a negative impact on society, and hate speech may have a direct correlation with real hate crimes, which increases the threat associated with being targeted by hate speech and abusive language. This study is the first to analyze hate speech in the context of Arabic COVID-19-related tweets in the Arab region.

Keywords: CNN; COVID-19; NMF; Twitter; convolutional neural network; coronavirus; deep learning; hate speech; non-negative matrix factorization; pandemic; public health; social media; social network analysis.

Figure 1

Methodology workflow. CNN: convolutional neural network; NMF: utilized nonnegative matrix factorization.

Figure 2

Number of hate tweets (red) and total tweets (black) in each Arab country, where a darker color depicts a higher number of hate tweets in that country (MA: Morocco, MR: Mauritania, DZ: Algeria, TN: Tunisia, LY: Libya, EG: Egypt, JO: Jordan, LB: Lebanon, SY: Syria, IQ: Iraq, SA: Saudi Arabia ,YE: Yemen, KW: Kuwait, QA: Qatar, AE: United Aran Emirates, OM: Oman).

Figure 3

Number of COVID-19–related hate tweets per country with the average hate level scores in brackets (low: 0.50-0.67; average: 0.68-0.85; high: 0.86-1.00). UAE: United Arab Emirates.

Figure 4

Numbers of hate tweets and numbers of COVID-19 cases and deaths per time period with the average hate level scores in brackets (low: 0.50-0.67; average: 0.68-0.85; high: 0.86-1.00).