Counterspeech Impact: Lessons Learned and the Path to Scalable Interventions

Table of Links

Abstract and 1 Introduction

2 Background

3 Review Methodology

4 Defining counterspeech

4.1 Classifying counterspeech

5 The Impact of Counterspeech

6 Computational Approaches to Counterspeech and 6.1 Counterspeech Datasets

6.2 Approaches to Counterspeech Detection and 6.3 Approaches to Counterspeech Generation

7 Future Perspectives

8 Conclusion, Acknowledgements, and References

7 Future Perspectives

Of the many promising abuse intervention experiments that we review, results are not always consistent, demonstrating weak claims or limited success (applicable only to certain settings). Possible reasons include short-term experiments, small sample sizes and non-standardised experimental designs. To improve this, effective interventions should come with the characteristics of scalability, durability, reliability, and specificity. In this section, we highlight key distinctions and overlaps across areas that have and have not been explored in social sciences and computer science, discuss ethical issues related to evaluating counterspeech in real-life settings and automating the task of counterspeech generation, and identify best practices for future research.

Distinctions and overlaps across areas By recognizing the commonalities and differences between social sciences and computer science, we pinpoint the unique contributions of each discipline and encourage interdisciplinary collaborations to address complex societal challenges and better understand human behaviour with the help of computational systems.

• Terminological clarity. Throughout the counterspeech literature, terminology is used inconsistently. Terms such as counterspeech and counter-narratives are often used interchangeably or used to refer to similar concepts. In social science, counterspeech is used to refer to content that disagrees with abusive discourses and counter-narratives often entail criticism of an ideology with logical reasoning. As a result, counter-narrative stimuli designed in social experiments are generally long form (Bélanger et al., 2020). In computer science on the other hand, the distinctions between counterspeech and counter-narratives have been vague, and training data is generally short form (while this may be bound by character limit on social media platforms). For instance, short and generic responses such as ‘How can you say that about a faith of 1.6 billion people?’ can be commonly found in counter-narrative datasets (Chung et al., 2019).

• The focus of evaluation. Social scientists and counterspeech practitioners generally attempt to understand and assess the impact of counterspeech on reducing harms (e.g., which strategies are effective and public perception towards counterspeech), whereas computer scientists focus more on technical exploration of automated systems and testing their performance in producing counterspeech (e.g., comparing system outputs with a pre-established ground truth or supposedly ideal output). One commonality between the social science and computer science studies is that most findings are drawn from controlled and small-scale studies. Applying interventions to real-world scenarios is a critical next step.

• Datasets. Dataset creation is an important component in computer science for developing machine learning models for generating counterspeech, while such contributions are less commonly considered in social sciences which rely on experiments using hand-crafted stimuli and one-time analyses of their effectiveness.

• Scope of research. We observe that, while computer scientists have focused on responses to abusive language and hate speech, social science studies address a wider range of phenomena, in particular radicalisation and terrorist extremism. It can be difficult to measure the effectiveness of counterspeech in challenging these over the short term, leading to some of the differences in evaluation metrics across disciplines.

• Lack of standardised methodologies. A variety of methodologies have been adopted in the literature, making comparisons across studies difficult. Without standardised evaluations, it is difficult to situate the results and draw robust findings. Ethical Issues, Risks and Challenges of Conducting Counterspeech Studies Effective evaluation of counterspeech not only identifies users who may need help, but also safeguards human rights and reinforces a stronger sense of responsibility in the community. This discussion is based on the authors’ opinion and not stemming from the review.

• Evaluating counterspeech in real-life settings Conducting the evaluation of counterspeech in real-world scenarios appears to provide a proactive and quick overview of its performance on hate mitigation. However, from an ethical perspective, the debate surrounding it is ongoing and reaching an agreement can be difficult. For instance, one side argues about the morality of exposing participants to harm, while another points to the importance of internet safety. Exercising counterspeech can offer mitigation of online abuse in good faith and may be exempt from liability based on several legal groundings. As an example, Good Samaritan laws provide indemnity to people who assist others in danger (Smits, 2000). In 2017 the EU Commission released a communication on tackling illegal content online, stating that ‘This Communication ... aims to provide clarifications to platforms on their liability when they take proactive steps to detect, remove or disable access to illegal content (the so-called “Good Samaritan” actions)’ (Commission, 2017). Section 230(c)(2) of Title 47 of the United States Code extents this protection to the good faith removal or moderation of third-party material they deem “obscene, lewd, lascivious, filthy, excessively violent, harassing, or otherwise objectionable, whether or not such material is constitutionally protected.” and stresses liability towards online hate speech. It protects online computer services from liability for moderating thirdparty materials that are harmful (Ardia, 2009; Goldman, 2018). The aim of these safeguards is to ensure that individuals are not hesitant to help others in distress due to the fear of facing legal consequences in case of unintentionally making errors in their efforts to provide support.

Responsible open-source research can facilitate reproducibility and transparency of science. Recently, reproducible research has been deemed critical in both social sciences (Stroebe et al., 2012; Derksen and Morawski, 2022) and computer science, and low replication success is found despite using materials provided in the original papers (Belz et al., 2023; Collaboration, 2015). To tackle this issue, a few initiatives for transparent research have been proposed, advocating researchers to state succinctly in papers how experiments are conducted (e.g., stimuli, mechanisms for data selection) and evaluated, including A 21 Word Solution (Simmons et al., 2012) and Open Science Framework.[5] Furthermore, practising data sharing encourages researchers to be responsible for fair and transparent experimental designs, and to avoid subtle selection biases that might affect substantive research questions under investigation (Dennis et al., 2019). At the same time, when handling sensitive or personal information, data sharing should adhere to research ethics and privacy standards (Dennis et al., 2019; de la Cueva and Méndez, 2022). For instance, in the case of hate speech, using synthetic examples or de-identification techniques is considered a good general practice for ensuring the safety of individuals (Kirk et al., 2022).

• Automating counterspeech generation There are several ethical challenges related to automating the task of counterspeech generation. First of all, there is the danger of dual-use: the same methodology could also be used to silence other voices.

Furthermore, effective and ethical counterspeech relies on the accuracy and robustness of detecting online hate speech: an innocent speaker may be publicly targeted and shamed if an utterance is falsely classified as hate speech – either directly or indirectly as in end-to-end response generation. For example, Google’s Jigsaw API (Google Jigsaw, 2022), a widely used tool for detecting toxic language, makes predictions that are aligned with racist beliefs and biases—for example it is less likely to rate anti-Black language as toxic, but more likely to mark African American English as toxic (Sap et al., 2022). It is thus important to make sure that the underlying tool is not biased and well-calibrated to the likelihood that an utterance was indeed intended as hate speech. For example, the ‘tone’ of counterspeech could be used to reflect the model’s confidence.

Furthermore, effective and ethical counterspeech relies on the accuracy and robustness of detecting online hate speech: an innocent speaker may be publicly targeted and shamed if an utterance is falsely classified as hate speech – either directly or indirectly as in end-to-end response generation. For example, Google’s Jigsaw API (Google Jigsaw, 2022), a widely used tool for detecting toxic language, makes predictions that are aligned with racist beliefs and biases—for example it is less likely to rate anti-Black language as toxic, but more likely to mark African American English as toxic (Sap et al., 2022). It is thus important to make sure that the underlying tool is not biased and well-calibrated to the likelihood that an utterance was indeed intended as hate speech. For example, the ‘tone’ of counterspeech could be used to reflect the model’s confidence.

In sum, there is a trade-off between risks and benefits of counterspeech generation. Following the ‘Good Samaritan’ law: automating counterspeech provides timely help to victims in an emergency which is protected against prosecution (even if it goes wrong). Similar legislation is adopted by other countries, including the European Union, Australia and the UK. Under this interpretation, well-intentional counterspeech (by humans and machines) is better than doing nothing at all.

Best practices We provide best practices for developing successful intervention tools.

1. Bear in mind practical use cases and scenarios of hate-countering tools. A single intervention strategy is unlikely to diminish online harm. To design successful counterspeech tools, it is important to consider the purposes of counter messages (e.g., support victims and debunk stereotypes), the speakers (e.g., practitioners, authorities and high-profile people), recipients (e.g., ingroup/outgroup, political background and education level), the content (e.g., strategy, style, and tones), intensity (e.g., one message per week/month), and the communication medium (e.g., videos, text, and platforms).

   
   

2. Look beyond automated metrics and consider deployment settings for evaluating the performance of generation systems. Generation systems are generally evaluated on test sets in a controlled environment using accuracy-based metrics (e.g., ROUGE and BLEU) that cannot address social implications of a system. Drawn from social science studies, metrics assessing social impact (e.g., user engagement), behavioural change (e.g., measure abuse reduction in online discourse) and attitude change (e.g., through self-description questionnaires) can be considered. A good intervention system is expected to pertain long-lasting effects.

   
   

3. Be clear about the methodology employed in experiments, open-source experimental materials (e.g., stimuli, questionnaires and codebook), and describe the desirable criteria for evaluating counterspeech intervention. As standardised procedures are not yet established for the assessment of counterspeech interventions, examining the impact of interventions becomes difficult. A meaningful description of experimental design would therefore enhance reproducible research and help capture the limitation of existing research.

   
   

4. Establish interdisciplinary collaboration across areas such as counter-terrorism, political science, psychology and computer science. AI researchers can help guide policymakers and practitioners to, for instance, identify long-term interventions by performing large-scale data analysis using standardized procedures on representative and longitudinal samples. With expertise in theories of human behaviour change and experimental design, social science researchers can conduct qualitative evaluations of AI intervention tools in real-life scenarios to understand their social impact.

8 Conclusion

Online hate speech is a pressing global issue, prompting scientists and practitioners to examine potential solutions. Counterspeech, content that directly rebuts hateful content, is one promising avenue. While AI researchers are already beginning to explore opportunities to automate the generation of counterspeech for the mitigation of hate at scale, research from the social sciences points to many nuances that need to be considered regarding the impact of counterspeech before this intervention is deployed. Taking an interdisciplinary approach, we have attempted to synthesize the growing body of work in the field. Through our analysis of extant work, we suggest that findings regarding the efficacy of counterspeech are highly dependent on several factors, including methodological ones such as study design and outcome measures, and features of counterspeech such as the speaker, target of hate, and strategy employed. While some work finds counterspeech to be effective in lowering further hate generation from the perpetrator and raising feelings of empowerment in bystanders and targets, others find that counterspeech can backfire and encourage more hate. To understand the advantages and disadvantages of counterspeech more deeply, we suggest that empirical research should focus on testing counterspeech interventions in real-world settings which are scalable, durable, reliable, and specific. Researchers should agree on key outcome variables of interest in order to understand the optimal social conditions for producing counterspeech at scale by automating its generation. We hope that this review helps make sense of the variety of types of counterspeech that have been studied to date and prompts future collaborations between social and computer scientists working to ameliorate the negative effects of online hate.

Acknowledgements

We thank Bertie Vidgen for the valuable feedback on the initial structure of this manuscript and Hannah Rose Kirk for her help with the collection of target literature.

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[5] https://osf.io/