Conflating Hate and Offence in Abusive Language Detection

Temel Kavramlar

The author argues against conflating hate and offense in abusive language detection, emphasizing the importance of distinguishing between the two concepts to avoid invalidating research findings on hate speech.

Özet

The content delves into the complexities of annotator subjectivity in natural language processing tasks related to online abuse detection, particularly focusing on isms like racism, sexism, and homophobia. The author highlights the need to disentangle hate from offense to ensure accurate identification of hate speech. The discussion covers various aspects such as stereotypes, social norms, annotator competency, and recommendations for improved schema design.

İstatistikler

"Dataset labelling in NLP is typically performed by annotators recruited either as crowd-sourced workers (e.g. Abercrombie et al., 2023; Basile et al., 2019; Fersini et al., 2018), academics or students available to the researchers (e.g. Cercas Curry et al., 2021; Fanton et al., 2021; Jiang et al., 2022), or people deemed to hold expertise in the phenomena (e.g. Talat, 2016; Vidgen et al., 2021; Zeinert et al., 2021)."
"Larimore et al. (2021) found that white annotators were far less competent in identifying anti-Black racism than Black annotators."
"Gordon et al. (2022) attempt to pick out the ‘correct’ minority perspectives from the wider pool of annotators for each instance."
"Fleisig et al. (2023) specifically assume that the majority of annotators are likely ‘wrong’, i.e., they will not recognize the target phenomenon."

Alıntılar

"Understanding isms as culturally defined, and offence as individually subjective allows us to distinguish any offence caused to a reader from whether a message contains hate speech."
"We recommend that schema be designed to carefully delineate these concepts, by e.g., creating distinct categories, and labeling them separately."
"If a minority with the necessary lived experience disagree with the majority who don’t, that matters."

Önemli Bilgiler Şuradan Elde Edildi

Subjective $\textit{Isms}$? On the Danger of Conflating Hate and Offence in Abusive Language Detection

by Amanda Cerca... : arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.02268.pdf

Daha Derin Sorular

How can societal norms influence the perception of offensive language?

Societal norms play a significant role in shaping how offensive language is perceived. These norms dictate what is considered acceptable or unacceptable behavior within a given society, influencing individuals' reactions to certain types of language. For example, derogatory terms or slurs that target specific groups may be deemed highly offensive in one culture but might be more tolerated or even normalized in another. The prevailing attitudes, values, and beliefs within a society create a framework through which individuals interpret and respond to language.
Furthermore, societal norms also impact the consequences of using offensive language. In some cultures, certain words or expressions are strictly prohibited due to their historical context or association with discrimination and prejudice. Violating these norms can lead to social ostracization, legal repercussions, or other forms of punishment. On the other hand, in societies where such restrictions are less stringent, offensive language may not carry the same weight of condemnation.
The evolution of societal norms over time also influences how offensive language is perceived. As communities progress towards greater inclusivity and sensitivity towards marginalized groups, certain forms of speech that were once accepted may become increasingly taboo. This shift reflects changing attitudes towards equality and respect for diversity within society.

How can advancements in NLP contribute to more accurate identification of hate speech while considering cultural contexts?

Advancements in Natural Language Processing (NLP) have the potential to enhance the accuracy of identifying hate speech by taking into account cultural contexts. By developing sophisticated algorithms that can analyze text data at scale, NLP technologies enable researchers to detect patterns and linguistic markers indicative of hate speech across different languages and communication styles.
One way NLP contributes to this effort is by incorporating machine learning models trained on diverse datasets that capture variations in cultural expressions and nuances related to hate speech. These models can learn from large volumes of annotated data containing examples of hateful rhetoric specific to various cultural backgrounds, allowing them to recognize subtle contextual cues that signal discriminatory intent.
Moreover, advancements in sentiment analysis techniques within NLP enable systems to differentiate between mere offensiveness and genuine hate speech based on the underlying tone and intention conveyed in textual content. By leveraging sentiment analysis tools alongside specialized hate speech detection algorithms, NLP researchers can develop more nuanced approaches for identifying harmful language while considering the socio-cultural factors that shape its interpretation.
Additionally, interdisciplinary collaborations between NLP experts and sociolinguists/cultural scholars can provide valuable insights into how different communities perceive hate speech based on their unique cultural frameworks. By integrating qualitative research methods with computational analyses, NLP practitioners can refine their models to better align with diverse cultural sensitivities regarding discriminatory discourse.

What challenges might arise when recruiting annotators with relevant profiles for labeling data?

Recruiting annotators with relevant profiles for labeling data poses several challenges that could impact the quality and consistency of annotations:
1- Lack of Diversity: Annotator pools may lack diversity concerning factors like race/ethnicity,
gender identity/expression etc., leading
to biased interpretations.
2- Limited Availability: Finding annotators who possess both domain expertise (e.g., understanding
of sexism/racism) as well as annotation skills
can be challenging due
to limited availability.
3- Annotator Bias: Even if recruited annotators have relevant profiles,
they may still exhibit personal biases
that affect their labeling decisions,
leading
to inconsistent annotations.
4- Training Requirements: Annotators often require extensive training
to ensure they understand task guidelines
and criteria thoroughly,
which adds complexity
and time constraints.
5- Annotator Turnover: High turnover rates among annotators could disrupt continuity
in labeling efforts,
requiring constant recruitment
and retraining processes.
6- Cost Considerations: Recruiting specialized annotators comes at an additional cost,
especially if they demand higher compensation due
to expertise requirements.
Addressing these challenges necessitates careful planning,
robust training protocols,and ongoing monitoring mechanisms
to maintain annotation quality throughout projects involving sensitive topics like hate speech detection.