C rowd C ounter: A benchmark type-specific multi-target counterspeech dataset (2024)

Abstract: Counterspeech presents a viable alternative to banning or suspending users for hate speech while upholding freedom of expression. However, writing effective counterspeech is challenging for moderators/users. Hence, developing suggestion tools for writing counterspeech is the need of the hour. One critical challenge in developing such a tool is the lack of quality and diversity of the responses in the existing datasets. Hence, we introduce a new dataset - CrowdCounter containing 3,425 hate speech-counterspeech pairs spanning six different counterspeech types (empathy, humor, questioning, warning, shaming, contradiction), which is the first of its kind. The design of our annotation platform itself encourages annotators to write type-specific, non-redundant and high-quality counterspeech. We evaluate two frameworks for generating counterspeech responses - vanilla and type-controlled prompts - across four large language models. In terms of metrics, we evaluate the responses using relevance, diversity and quality. We observe that Flan-T5 is the best model in the vanilla framework across different models. Type-specific prompts enhance the relevance of the responses, although they might reduce the language quality. DialoGPT proves to be the best at following the instructions and generating the type-specific counterspeech accurately.

## introduction the proliferation of hate speech and offensive language has become a significant problem in the current society @xcite . efforts to moderate such content using banning and suspension are ineffective as users might shift to other platforms @xcite . further, banning/suspension hampers the principles of freedom of speech (ullmann and tomalin, 2020). hence, social scientists are focusing on alternative forms of mitigation strategies, one of which is counter-speech. it is a response to abusive or hateful language in the form of constructive and persuasive responses. while counterspeech presents itself as a viable alternative following the principles of freedom of expression, it comes with challenges. a major challenge is the onus on the moderators or the users to write a good counterspeech @xcite . hence, researchers across the globe are attempting to develop nlg-based suggestion tools to help moderators craft counterspeech. one major challenge of building such tools is a good quality and diverse abusive speech-counterspeech pair dataset. few of the past datasets use synthetically generated hate speech @xcite , while others are not very diverse in terms of abusive speech targets @xcite or types of counterspeech @xcite . few of the approaches require experts @xcite , which makes them less scalable. hence, we prepare a dataset -crowd-counter following the steps listed below. using this dataset, we built two prompting frame-works -vanilla and type-specific prompts, for generating counterspeech using four models. in the vanilla prompt approach, we also compare two parallel hatespeech-counterspeech datasets -gab and reddit @xcite . we evaluate the generated responses using three different categories of metrics -referential, diversity and quality. we make the following observations. ## related works counterspeech @xcite has been proposed as an effective mitigation strategy for hate speech @xcite @xcite . one of the earliest works @xcite collected abusive language from gab and reddit and asked the crowd annotators to provide the counterspeech. few other datasets @xcite rely on expert annotations. one of the key problems of both these datasets is that the hate speech instances are generated synthetically; hence, a counterspeech generation system built on this cannot be deployed on actual social media platforms. as highlighted by @xcite , different strategies/types are helpful while writing an effective counterspeech. @xcite curated a dataset of counterspeech, where each instance was annotated by the type(s) they cor-responded to. another dataset @xcite also contains types annotated along with the counterspeech provided; however, it is only limited to islamophobic content. recently, @xcite re-annotated the counterspeech instances from a past work @xcite with type-specific annotation. we had difficulty accessing the dataset for our benchmarking. the data was not available in the mentioned repositoryhttps://github.com/lcs2-iiitd/ quarc-counterspeech , and we did not receive responses to our emails requesting it. another paper @xcite focused on creating counterspeech in zero-shot setting and tries to create typespecific counterspeech using type-specific prompts. although this is a step in the right direction, prompt based control provides limited flexiibility. finally, we were dismayed by not being able to retrieve the dataset and use it for our benchmarking experiments. we did not find the dataset (as claimed by the authors) in the repository associated with the paper - https://github.com/lcs2-iiitd/ quarc-counterspeech ; moreover, the authors did not respond to our e-mails requesting the data. in our paper, we attempt to address the limitations of the past research and present a dataset of abusive speech-counterspeech pairs crowd-counter. the abusive speech in this dataset is naturally occurring (from either x or gab) and is diverse in terms of the number of targets. while the counterspeech is crafted by crowd annotators, we introduced a series of techniques to avoid the pitfalls of crowd-based annotations. the annotators were tasked to craft the counterspeech instances of different types (warning of consequences, shaming/labeling, empathy/affiliation, humor, contradiction and questions) unlike in @xcite where the annotators had to label an existing counterspeech with a type thus severely limiting the expression of their own opinion. ## dataset curation in this section, we discuss the details of how crowdcounter was curated. specifically, we discuss how we sampled the abusive language dataset, the design of the annotation platform, the selection of annotators and the final dataset curation. we employ annotators from amazon mechanical turk ( https://www.mturk.com/ ), one of the popular annotation platforms. the following subsections provide an in-depth overview of the key steps and considerations in our dataset curation process. ## other datasets here, we note the other crowd-sourced hate speechcounterspeech pairs (hs-cs) datasets that were used to compare with our dataset. we also note the curation of an additional dataset, which was used to build the multilabel type classifier (section 7). ## models here, we briefly mention the models utilized in this work for counterspeech generation or counterspeech-type classification. bert @xcite :bert is a pre-trained language model that has revolutionized natural language processing tasks. developed by google ai researchers, bert's bidirectional training approach allows it to understand the context better, leading to improved performance @xcite . we use the bert-base-uncased foot_4 model having 110m parameters. this model is used for counterspeech-type classification. dialogpt (zhang et al., 2020): dialogpt (zhang et al., 2020) is a dialogue-centric language model developed by microsoft, derived from the gpt-2 architecture and fine-tuned on a large dataset of reddit conversations. it generates human-like, contextually relevant responses in multi-turn dialogues, making it well-suited for conversational ai applications like chatbots and dialogue systems. we use the dialogpt-medium foot_5 , which has 250m parameters. this model is used for counterspeech generation. flan-t5 @xcite : flant5 is a large language model developed by google that builds upon the t5 encoder-decoder architecture. it was trained on a vast and diverse corpus using a unified textto-text framework, enabling strong performance across a wide range of natural language processing tasks. flan-t5's massive scale and innovative training approach have pushed the boundaries of few-shot learning, allowing it to adapt quickly to new tasks with just a few examples. we use the flan-t5-base foot_6 having 250m parameters. this model was used for both counterspeech generation and counterspeech type classification. llama @xcite : llama is a finelytuned generative text model designed by meta. these are trained on a diverse mix of publicly available online data between january 2023 and july 2023, and this model utilizes supervised fine-tuning (sft) and reinforcement learning with human feedback (rlhf) to align with human preferences for helpfulness and safety. we used the llama-2-7b-chat-hf foot_7 and the recent meta-llama-3-8b-instruct foot_8 for counterspeech generation. while the former is tuned for chatspecific scenarios, the latter is better in following instructions. we use the 4-bit quantized version of these models along with lora @xcite to train these models. ## metrics broadly, the metrics in this paper can be divided into three parts -referential, diversity and quality metrics. diversity and quality metrics do not require the ground truth. ## conclusion in conclusion, we create the first ever type-specific, diverse and crowd-sourced abusive-counterspeech pairs -crowdcounter. we trained four language models in two different frameworks i.e., vanilla and type-specific prompting. we evaluated the responses generated by these models along the dimensions of relevance, diversity and quality. we notice that compared to other crowd-sourced datasets, i.e., gab and reddit, crowdcounter has higher diversity and quality. in terms of vanilla generation, finetuned flan-t5 is quite superior to even larger models from the llama family while being 32x smaller than them. constraining the models to generate a particular type of counterspeech does improve the relevance of their outputs but also reduces the language quality to some extent. finally, dialogpt is quite proficient at following the type-specific instructions better than all the other models. examples of generations are added in appendix