The Challenge of Temporal Bias in Abusive Language Detection

The rise of online social media has brought many benefits, but it has also led to serious issues, including the spread of abusive language. This type of language can harm individuals and contribute to division within society. As a response to this problem, researchers have created machine learning models designed to automatically identify abusive language on various platforms. However, these models can face challenges due to something known as temporal bias. Temporal bias happens when language, topics, or social norms change over time, making it difficult for models trained on older data to recognize new patterns.

This article aims to examine how temporal bias affects the detection of abusive language across different languages and to investigate strategies that may help reduce this bias. By looking at data collected from various time periods and languages, we can better understand the challenges faced by these detection models.

#What is Temporal Bias?

Temporal bias refers to the idea that language and topics evolve as society progresses. For instance, certain phrases or words that were once accepted may now carry offensive connotations due to shifting cultural norms. In the context of online abuse detection, this can lead to models missing abusive content or misclassifying non-abusive content as abusive. This bias is particularly problematic when discussing social issues where the context can shift rapidly, like current events or popular culture.

One key aspect of temporal bias is Concept Drift, which occurs when the nature of data changes over time. For example, the rise of new slang or changes in societal attitudes can make older models ineffective. Models trained on past data may struggle to adapt to new linguistic trends, leading to poorer performance when applied to recent data.

#The Impact of Social Media

The increasing prevalence of social media has amplified the use of abusive language. This has resulted in significant harm to both individuals and groups, as well as contributing to broader social tensions. Researchers have developed various machine learning models to detect and mitigate abusive language. However, many of these models were built using older datasets that may not accurately reflect the current state of language or social norms.

Temporal bias can cause these models to miss new forms of abuse or misinterpret language that has changed meaning. Therefore, there is a pressing need to study how temporal bias affects these models and explore ways to improve their accuracy.

#The Study

This study investigates temporal bias across different abusive language datasets in multiple languages: English, Spanish, Italian, and Chinese. The primary research questions focus on the extent of temporal bias, the types of language evolution contributing to it, and the effectiveness of various mitigation strategies.

#Research Questions

1. How does the severity of temporal bias vary across different datasets, including language, time periods, and methods of collection?
2. What forms of language evolution contribute to temporal bias in our datasets?
3. Can using updated models, larger datasets, or Domain Adaptation techniques reduce temporal bias in detecting abusive language?

To address these questions, researchers will compare the Predictive Performance of various models on datasets spanning different time periods. This will help identify the scale of the challenge posed by temporal bias.

#Previous Work

Several studies have looked at bias in language processing, but few have specifically focused on temporal bias in abusive language detection. Most existing research has centered on other forms of bias, such as gender or identity bias, but the dynamics of temporal bias are less understood.

Earlier research indicated that adding contemporary data to models enhances their performance. For instance, some studies have shown that the performance of abusive language detection can improve by including more recent data, while simply expanding the dataset size without temporal relevance does not yield the same benefits.

In the context of classification tasks, several studies have explored how temporal bias affects various language processing applications. These studies highlighted the significance of using time-aware models that can adapt to changes in language usage over time.

#Datasets Used

For this investigation, researchers used five datasets focused on abusive language, with a focus on different languages:

1. WASEEM: An English dataset centered on sexism and racism, comprising tweets collected through targeted searches.
2. FOUNTA: Another English dataset from Twitter, which includes abusive and hateful content collected using a combination of sampling methods.
3. JIANG: A Chinese dataset focused on gender-related abuse, collected from a popular Chinese microblogging platform.
4. PEREIRA: A Spanish dataset that encompasses various types of hate speech, manually annotated by experts.
5. SANGUINETTI: An Italian dataset targeting hate speech against immigrants, collected through keyword searches.

Each dataset was analyzed for its temporal aspects, as they contain timestamps or creation dates for each abusive post. This allowed researchers to examine how detection models performed using data from different periods.

#Data Processing

To ensure the datasets were appropriate for analysis, several steps were taken:

1. Data Filtering: In some cases, tweets that lacked creation dates or relevant content were removed.
2. Data Splits: Researchers divided the datasets into training and testing sets, employing both random splits and chronological splits. The goal was to compare how models performed under different conditions.

#Random Splits

In random splits, the data was shuffled and divided into training and testing sets while maintaining the original class distribution. This approach does not take the order of data into account.

#Chronological Splits

Chronological splits sorted the data by time, retaining the first two-thirds for training and the remaining third for testing. This method aimed to simulate real-world scenarios where models are deployed to detect abusive language in ongoing discussions.

#Predictive Models

To evaluate the datasets, researchers employed several machine learning models, including:

1. Logistic Regression: A basic model using a bag-of-words approach.
2. BERT: A transformer-based language model that predicts masked words based on context, fine-tuned for abusive language detection.
3. RoBERTa: An extension of BERT, trained on larger datasets with variations in training parameters.
4. RoBERTa-hate-speech: A model adapted specifically for hate speech detection in English.

These models were assessed based on their ability to accurately classify abusive and non-abusive tweets.

#Experimental Setup

Researchers pre-processed the tweets by replacing user mentions and links with placeholders. They utilized appropriate tokenization techniques for different languages to ensure the models could analyze the text effectively.

Models were fine-tuned using various hyperparameters, and performance was evaluated based on metrics like accuracy, precision, recall, and macro-F1 scores. Multiple trials were conducted to ensure the robustness of results.

#Results

The findings indicated that models generally performed better with random splits compared to chronological splits. As time spans increased between training and testing datasets, performance decreased. This performance drop is significant because it highlights how the temporal context affects the ability of models to make accurate predictions.

#Observations on Performance

1. Performance Degradation: Models showed notable performance declines when trained on older data and tested on newer instances. For datasets with longer time spans, the drop in accuracy was more profound.
2. Domain Adaptation: Models specifically trained to handle hate speech fared better across datasets as they could adapt to the evolving language used in abusive contexts.
3. Impact of Language: Language differences influenced model performance, with some languages showing greater resilience against performance drops than others.

The research found a strong correlation between the time span of data and the predictive performance of models, emphasizing the need for continual learning and adaptation to language changes.

#Linguistic Analysis

An analysis of linguistic patterns provided insights into why certain models faltered. It revealed that the introduction of new topics or events not present in the training data often led to misclassifications. In datasets where recent events dominated the conversation, models struggled to recognize and classify abusive content accurately.

#Topic Distribution

Researchers conducted a topic modeling analysis to identify the most common themes present in abusive tweets across the datasets. This analysis highlighted how certain topics gained prominence at different times, further supporting the notion that temporal context is crucial for effective abusive language detection.

#Mitigation Strategies

The study explored possible strategies to combat temporal bias. One approach included filtering datasets to remove words associated with specific events, aiming to create a more uniform linguistic environment for model training. This strategy yielded mixed results, indicating that while it could help reduce performance drops, it also risked diminishing overall performance.

#Domain Adaptation Models

The use of domain adaptation techniques proved beneficial in reducing temporal bias. These models, trained on a variety of datasets that included current abusive language trends, demonstrated improved accuracy across both random and chronological splits.

#Conclusion

This research underscores the significant impact of temporal bias on abusive language detection models. It reveals how changing societal norms and language can hinder the effectiveness of these models over time. As our communication evolves, so must the tools we rely on to ensure safe online environments.

Future work will expand upon these findings by investigating temporal bias across different platforms and languages. It will also look to develop more adaptive models that can adjust to ongoing changes in language and context. By taking these steps, we can better equip our systems to handle the complexities of online communication and create safer spaces for all users.

#Ethics Statement

This study adhered to ethical guidelines to ensure that research practices respected privacy norms and contributed positively to society. All data utilized was anonymized and sourced from public datasets, ensuring that no new users were involved in the research process. By following these guidelines, the study aims to contribute to the responsible development of models for abusive language detection.