From Generic to Personalized: Investigating Strategies for Generating Targeted Counter Narratives against Hate Speech (2023)

Abstract: The spread of hate speech (HS) in the digital age poses significant challenges, with online platforms becoming breeding grounds for harmful content. While many natural language processing (NLP) studies have focused on identifying hate speech, few have explored the generation of counter narratives (CNs) as means to combat it. Previous studies have shown that computational models often generate CNs that are dull and generic, and therefore do not resonate with hate speech authors. In this paper, we explore the personalization capabilities of computational models for generating more targeted and engaging CNs. This paper investigates various strategies for incorporating author profiling information into GPT-2 and GPT-3.5 models to enhance the personalization of CNs to combat online hate speech. We investigate the effectiveness of incorporating author profiling aspects, more specifically the age and gender information of HS authors, in tailoring CNs specifically targeted at HS spreaders. We discuss the challenges, opportunities, and future directions for incorporating user profiling information into CN interventions.

## introduction hate speech (hs) is spreading through various online channels, targeting vulnerable individuals and minority groups, and leading to discrimination and division. the spread of hs can be motivated by fear, ignorance, and prejudice, with severe consequences such as increased suicide rates (o' @xcite . since there is a fine line between free speech and hate speech, monitoring the removal or blocking of online hs is challenging and may lead to over-blocking @xcite . instead of enforcing silence through blocking, "more speech" can be used, e.g., through the generation of counter narratives (cns), which promotes understanding and different perspectives. @xcite defines counter narratives as "non-negative responses to hate speech, targeting and counteracting extreme statements through fact-bound arguments or alternative perspectives". counter-narrative generation plays a crucial role in educating and enlightening those who may be influenced by hate speech or exposed to biased narratives @xcite . providing well-articulated and evidence-based responses, counter narratives can expose the fallacies and misinformation propagated by hate speech, enabling individuals to make informed judgments and decisions. ngos work for exactly this purpose by training operators to write counter narratives 1 , however, there is a great imbalance between the number of hate speech shared every day and the responses that can be written by ngo operators. to tackle this issue, natural language generation (nlg) studies have suggested automatic cn generation @xcite @xcite . on top of that, profiling hate speech authors has gained attention in natural language processing (nlp) and sociolinguistic communities, providing valuable insights into hs authors' motivations and enhancing hate speech detection systems by incorporating author profiling information @xcite @xcite . to the best of our knowledge, exploring author profiling aspects within personalized counter-narrative interventions remains an unexplored area of research despite its potential to effectively counter 1 https://getthetrollsout.org/stoppinghate 1 hate speech online. consider the following example with two possible cn responses to hateful content 2 : cn1: women should have equal rights in a free society, without discrimination. cn2: it can be difficult to adjust to a changing society, but it's important to recognize that gender equality and freedom are not just ideals; they are human rights that should be respected and upheld at all times. women should have the same rights and opportunities as men, without any discrimination or prejudice. older generations can help foster a culture of equality and respect by acknowledging that everyone deserves to be seen and treated equally, regardless of gender or any other background. cn2 is more personalized and likely to be more engaging for a hs author than cn1. we explore ways how to make counter-narrative interventions more personalized and effective in withstanding the propagation of online hate speech. our approach is based on replicating the study by @xcite with gpt-2 @xcite and exploring different strategies for injecting author profiling aspects into the model with the aim of generating more targeted counter narratives. additionally, we experiment with gpt-3.5 3 to investigate its potential for personalizing counter narrative based on profiling characteristics. findings suggest that while gpt-2 struggles with using the injected author profiling aspects to generate more personalized cns, gpt-3.5 has the ability to incorporate profiling information, such as authors' age and gender, to generate more personalized and engaging responses against hate speech. ultimately, this paper emphasizes the significance of effective counter narratives as a valuable tool for fostering a more inclusive and harmonious digital environment, free from the damaging effects of hate speech. ## methods the present research replicates the approach presented in the study by @xcite . we used the gpt-2 model @xcite that showed promising results with the hyperparameters and decoding mechanisms reported in the continuation of this section. we designed a suite of experiments to explore the effect of incorporating profiling information: using gpt-2 to generate counter narratives while utilizing profiling information exclusively in the test set, fine-tuning gpt-2 with profiling information, fine-tuning gpt-2 with more detailed profiling information, generating personalized cns using gpt-3.5 and fine-tuning gpt-2 with those personalized cns. in the continuation, we provide more details into strategies used to incorporate author profiling characteristics. ## evaluation we report both manual and automatic evaluation of generated cn responses. we realize that automatic evaluation metrics may not reflect the personalization aspects of the cns since we compare them to the gold cns from the dataset. accordingly, we also perform a manual evaluation, which ranks generated cns with respect to each other and with respect to the gold cns based on how personalized and engaging they are. ## discussion the utilization of gpt-2 was motivated by selecting one of the most promising outcomes from the aforementioned study @xcite . the objective was to employ this model to explore various approaches for incorporating profiling information about hate speech users, aiming to identify an optimal method for personalizing counter narratives. the findings of the conducted experiments revealed that gpt-2 encounters challenges when attempting to generate personalized counter narratives. this pattern persisted throughout the various strategies employed for incorporating profiling aspects of hs authors. these findings sug-gest that further investigation and refinement of the strategies are necessary to enhance gpt-2's ability to generate more personalized responses to hate speech. the results from both the automatic metrics and the manual evaluation reinforce the notion that employing a more advanced model exhibits a greater potential for transforming generic counter narratives into personalized and engaging ones. leveraging the strong prompt-prefixing capability of gpt-3.5, we successfully generated personalized and engaging counter narratives indicating that, even with simple prompts, gpt-3.5 can effectively generate targeted and effective counter narratives. in the future, more detailed prompt engineering can potentially improve the personalization of generated cns. factors such as removing items where age and gender are not relevant could have a significant impact on the results of this research since it also reduced the number of hs instances for training and testing. however, our main focus was finding the optimal approach to incorporate profiling information for personalization of cns. therefore, we conducted experiments using examples where age and gender potentially played a significant role (e.g., a man criticizing women) to observe the impact of each strategy, which provided valuable insights. once the best strategy is identified, the next step would involve applying it to more hate speech examples where age and gender could be relevant, paving the way for real-world applications. given our focus on gpt, it would be intriguing to conduct a comparative analysis to assess the performance of alternative models such as bart @xcite or t5 @xcite and recent llms such as llama @xcite and koala @xcite ) when injected with profiling information. in line with @xcite 's evaluation approach, the usage of personalized counter narratives generated with gpt-3.5 to combat online hate speech can additionally be evaluated by measuring user engagement, such as the number of likes or replies, to determine if there is a notable preference for personalized cns. ## conclusion the present study demonstrated that counter narratives can be made more personalized and engaging using the profiling characteristics of hate through incorporating author profiling information, we attempted to bridge the gap of generating more personalized counter narratives. small glimpses into relevant findings regarding the incorporation of user information are a promising basis for researchers interested in focusing on the generation of less generic counter narratives, or in general, combating hate speech effectively. ultimately, these findings shed light on the complex interaction between societal challenges such as hate speech in the digital realm, and the computational methods employed to tackle them. by examining the strengths and limitations of computational methods in addressing such challenges, we can strive towards more comprehensive and impactful strategies to promote a safer and more inclusive online environment.