Monitoring Hate Speech in I ndonesia: An NLP -based Classification of Social Media Texts (2024)

Abstract: Online hate speech propagation is a complex issue, deeply influenced by both the perpetrator and the target’s cultural, historical, and societal contexts. Consequently, developing a universally robust hate speech classifier for diverse social media texts remains a challenging and unsolved task. The lack of mechanisms to track the spread and severity of hate speech further complicates the formulation of effective solutions. In response to this, to monitor hate speech in Indonesia during the recent 2024 presidential election, we have employed advanced Natural Language Processing (NLP) technologies to create an improved hate speech classifier tailored for a narrower subset of texts; specifically, texts that target vulnerable groups that have historically been the targets of hate speech in Indonesia. Our focus is on texts that mention these six vulnerable minority groups in Indonesia: Shia, Ahmadiyyah, Christians, LGBTQ+, Indonesian Chinese, and people with disabilities, as well as one additional group of interest: Jews. The insights gained from our dashboard have assisted stakeholders in devising more effective strategies to counteract hate speech. Notably, our dashboard has persuaded the General Election Supervisory Body in Indonesia (BAWASLU) to collaborate with our institution and the Alliance of Independent Journalists (AJI) to monitor social media hate speech in vulnerable areas in the country known for hate speech dissemination or hate-related violence in the upcoming Indonesian regional elections. This dashboard is available online at https://aji.or.id/hate-speech-monitoring .

## introduction indonesia's history is marked by the use of hate speech to incite discrimination and violence @xcite . this speech, often amplified during times of political tension such as during an election, targets people or groups based on their race, gender, ethnicity, religion, sexual orientation, and disability. the advent of social media has exacerbated this issue, as evidenced by a ten-fold increase in hate speech ratio during the 2024 indonesian presidential election compared to @xcite @xcite . ## methodology in this work, we adopt the definition of hate speech set by indonesia's national human rights commission, which includes any communication motivated by hatred against people based on their identities, intending to incite violence, death, and social unrest @xcite . based on this definition and the domestic context of online hate speech and toxicity in indonesia, we define five types of hate speech and toxic text in our work: unlike prior hate speech detection efforts that focus primarily on detection models, we integrate insights from indonesian vulnerable group about common online attacks targeted towards them. this was achieved through focus group discussions (fgds), where we identified seven targeted vulnerable groups, comprising six minority groups: shia, ahmadiyyah, christians, lgbtq+ individuals, tionghoa, and people with disabilities, along with one additional group of interest: jews, due to the rising israeli-palestinian conflict. through the fgds, we obtain keywords that are often used online to refer to each minority group as well as keywords used to target each vulnerable group (listed in appendix a). using these keywords, we use brandwatch ( www. brandwatch.com ) to collect data mentioning the targeted vulnerable groups from x (formerly twitter), and the now-deprecated crowdtangle ( https: //crowdtangle.com/ ) to retrieve data from facebook and instagram. due to x's download limit, we use a sampling rate of 23%, implying that for each post we gathered from the platform, approxi-mately three posts were not collected. in collaboration with an indonesian fact-checking organization mafindo, we collect news articles containing misinformation that mention these groups from cekfakta's article database ( https://cekfakta. com/ ). the data totals 1.45 million texts (from 1 sep 23 to 27 mar 24). ## system description: content of the dashboard at the time of this paper's submission, our dashboard has processed over 1.45 million online texts mentioning the identified vulnerable groups, dating from 1 september 2023 to 27 march 2024, from facebook, x, instagram, and online articles. the dashboard, created using power bi, consists of the following 6 pages. the introduction page outlines the motivation behind this dashboard, what we define as hate speech, the time frame of interest, where the data originate from, the target groups we focus on, and how we create this dashboard. the overview page serves as the main summary of information. at the top of the page exists a slider to filter the data date range. additionally, there are three pie charts, each displaying the hate speech distribution, the distribution of texts related to the election (i.e., "related to pemilu 2024"), and the data source distribution. the hate speech trend page shows the quantity of hate speech over time on multiple social media platforms. we also add filter options to enhance analysis capability: the date filter, platform filter, and related-to-election filter. these filters are also available in the following two pages. figure 4: the type of hate speech page the type of hate speech page functions to map the type of hate speech-identity attack, insult, profanity, threat/incitement to violence, or vulgarity-that our model predicts in the dataset. since a text can potentially contain more than one type of hate speech, the total sum of data on this page will be above the hate speech count presented on the overview page. the targeted groups page shows the distribution of the targeted vulnerable groups in the detected hate speech. we also group these target groups into coarser categories such as ethnicity, religion, disability, and gender & sexuality. the engagement score page shows how much engagement hate speech texts collectively obtain from each platform. this page contains filters from previous pages, namely the (hate speech) target ## observation results from this monitoring tool, a non-exhaustive list of interesting observations can be made: the 2023 israel-hamas war has affected the circulation of hate speech targeting jews in indonesia, shown in figure 7 . before the war, which started on 7th october 2023, only 15k out of 189.9k (7.78%) texts were found to be hate speech. during this period, only 1.5k hate speech texts targeted jews, while chinese descendants in indonesia (the tionghoa ethnicity) had 4.1k hate speech texts targeting them. however, in november 2023, 42k out of 206.9k (20.21%) texts were found to be hate speech. during this period, hate speech texts against tionghoa ethnicity dropped to only 1.25k texts, while hate speech texts targeting jews sharply rose to 28k. this number means that two-thirds of hate speech texts in november 2023 targeted jews. though the ratio of hate speech circulating in march 2024 on social media has returned to its previous level in september 2023, the number of overall hate speech has increased. despite our constant sampling rate during data collection, the number of posts mentioning targeted vulnerable groups in indonesia has increased in recent months, as shown in figure 8 . so, even though technically the ratio of hate speech to non-hate speech text mentioning vulnerable groups in indonesia has fallen from 7.53% in september 2023 to 7.39% in march 2024, the total number of hate speech has increased from 12,465 to 16,395. note that we did update our keywords to collect texts mentioning the rohingya refugees in december 2023. some vulnerable groups are attacked for political reasons. filtering our dashboard to texts related to the 2024 indonesian presidential election, we see that the tionghoa ethnicity is often the target of political (i.e., related-to-election) hate speech, as shown in figure 9 . after the israel-hamas war, the prominent target of political hate speech shifted to jews. however, we noticed that during both the 4th and 5th presidential debates, aired on 21st january and 4th february 2024 respectively, the target of political hate speech returned to the tionghoa ethnicity for a short while. meanwhile, other vulnerable groups are attacked for non-political reasons. the top three vulnerable groups often being targeted by political hate speech are jewish, tionghoa, and lgbtq+ while the top three vulnerable groups often being targeted by hate speech in general are jewish, tionghoa, and christians. throughout the dashboard's monitoring, we only find 301 texts where christians are the target of political hate speech; meanwhile, they are targeted by over 9765 nonpolitical hate speech texts. ## conclusion and recommendation correctly fighting hate speech is hard. effective measures like stringent content filtering or social media bans should be reserved for extreme cases. but, knowing when we have reached those extreme cases is not trivial. this is why we reiterate the importance of a hate speech monitoring tool. the general election supervisory body in indonesia (bawaslu) has also monitored hate speech during indonesia's 2024 presidential election. however, theirs was done manually with human annotators monitoring and collecting posts on multiple social media platforms. as expected, this approach to monitor hate speech lacks scalability. comparatively, our dashboard allows for scalable monitoring, only requiring someone to download scraped social media posts and prepare them for the model to infer, which can be done by a single person. this was the basis of monash university indonesia's collaboration with bawaslu, underlining the importance of scalability and the application of nlp technologies for monitoring hate speech, which we explain further in the impact section of our work below. based on our dashboard's findings from the 2024 election, we urge stakeholders -social media platforms, election organizers, media, and journaliststo intensify their efforts to prevent and mitigate online hate speech, particularly during political events like general elections. our recommendations for social media platforms are as follows: election organizers must remember that hate speech is context-dependent; influenced by historical, societal, and cultural contexts. any action to prevent and mitigate hate speech must consider its impact on citizens' freedom of expression. controversial regulations like article 28 paragraph (2) of the 2016 indonesian ite law (law on electronic information and transactions), often misused to silence marginalized minority groups, necessitate the exploration of non-regulatory solutions. to this end, we recommend the following: lastly, for the media and journalists, we recommend the following: