Datasets:

thefrankhsu
/

hate_speech_twitter

@@ -15,10 +15,8 @@ tags:
 size_categories:
 - 1K<n<10K
 ---
-# Dataset Card for Dataset Name
 <!-- Provide a quick summary of the dataset. -->
 The dataset is designed to analyze and address hate speech within online platforms. It consists of two sets: the training and testing sets. The two datasets have been labeled and categorized instances of hate speech into nine distinct categories.
 ## Dataset Description
@@ -30,7 +28,6 @@ physical appearance, race, religion, sexual orientation).
 * Testing set: contains a total of 1000 tweets (Hate Speech: 500 / Non Hate Speech: 500), and the number of hate speech in each category is generally even.
 ## Uses
 This dataset can be utilized for various purposes, including but not limited to:
 * Developing and training machine learning models for hate speech detection.
 * Analyzing the prevalence and patterns of hate speech across different categories.
@@ -43,11 +40,10 @@ Check it out for the example [project](https://github.com/Wei-Hsi/AI4health)!
 The dataset utilized in this study is sourced from Kaggle and named the [Hate Speech and Offensive Language dataset](https://www.kaggle.com/datasets/mrmorj/hate-speech-and-offensive-language-dataset/).
 Hate speech instances are identified by selecting tweets within the "class" column.
-### Annotations
 <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
 Category labels were generated through an OpenAI API call employing the GPT-3.5 model.
 It's important to note the instability in category predictions when utilizing GPT-3.5 for label generation, as it tends to predict different categories each time. However, we have confirmed that these tweets were labeled correctly. If there are any misclassified labels, please feel free to reach out. Thank you in advance for your assistance.
 ## Dataset Card Contact
 Please feel free to contact me via wh476@cornell.edu!

 size_categories:
 - 1K<n<10K
 ---
+## Dataset Card for Dataset Name
 <!-- Provide a quick summary of the dataset. -->
 The dataset is designed to analyze and address hate speech within online platforms. It consists of two sets: the training and testing sets. The two datasets have been labeled and categorized instances of hate speech into nine distinct categories.
 ## Dataset Description
 * Testing set: contains a total of 1000 tweets (Hate Speech: 500 / Non Hate Speech: 500), and the number of hate speech in each category is generally even.
 ## Uses
 This dataset can be utilized for various purposes, including but not limited to:
 * Developing and training machine learning models for hate speech detection.
 * Analyzing the prevalence and patterns of hate speech across different categories.
 The dataset utilized in this study is sourced from Kaggle and named the [Hate Speech and Offensive Language dataset](https://www.kaggle.com/datasets/mrmorj/hate-speech-and-offensive-language-dataset/).
 Hate speech instances are identified by selecting tweets within the "class" column.
+## Annotations
 <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
 Category labels were generated through an OpenAI API call employing the GPT-3.5 model.
 It's important to note the instability in category predictions when utilizing GPT-3.5 for label generation, as it tends to predict different categories each time. However, we have confirmed that these tweets were labeled correctly. If there are any misclassified labels, please feel free to reach out. Thank you in advance for your assistance.
 ## Dataset Card Contact
 Please feel free to contact me via wh476@cornell.edu!