scholarly journals Psychosocial Features for Hate Speech Detection in Code-switched Texts

2021 ◽  
Vol 13 (6) ◽  
pp. 29-47
Author(s):  
Edward Ombui ◽  
◽  
Lawrence Muchemi ◽  
Peter Wagacha
Keyword(s):  
Language Processing ◽  
Presidential Elections ◽  
Hate Speech ◽  
Learning Models ◽  
Speech Detection ◽  
Word Families ◽  
Novel Approach ◽  
Duplex Theory ◽  
High Level ◽  
Speech Identification

This study examines the problem of hate speech identification in codeswitched text from social media using a natural language processing approach. It explores different features in training nine models and empirically evaluates their predictiveness in identifying hate speech in a ~50k human-annotated dataset. The study espouses a novel approach to handle this challenge by introducing a hierarchical approach that employs Latent Dirichlet Analysis to generate topic models that help build a high-level Psychosocial feature set that we acronym PDC. PDC groups similar meaning words in word families, which is significant in capturing codeswitching during the preprocessing stage for supervised learning models. The high-level PDC features generated are based on a hate speech annotation framework [1] that is largely informed by the duplex theory of hate [2]. Results obtained from frequency-based models using the PDC feature on the dataset comprising of tweets generated during the 2012 and 2017 presidential elections in Kenya indicate an f-score of 83% (precision: 81%, recall: 85%) in identifying hate speech. The study is significant in that it publicly shares a unique codeswitched dataset for hate speech that is valuable for comparative studies. Secondly, it provides a methodology for building a novel PDC feature set to identify nuanced forms of hate speech, camouflaged in codeswitched data, which conventional methods could not adequately identify.

scholarly journals Psychosocial Features for Identifying Hate Speech in Social Media Text

2021 ◽  
pp. 32-51
Author(s):  
Edward Ombui ◽  
Lawrence Muchemi ◽  
Peter Wagacha
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Presidential Elections ◽  
Hate Speech ◽  
Hierarchical Approach ◽  
Learning Models ◽  
Word Families ◽  
Social Media Text ◽  
Duplex Theory ◽  
High Level

This study uses natural language processing to identify hate speech in social media codeswitched text. It trains nine models and tests their predictiveness in recognizing hate speech in a 50k human-annotated dataset. The article proposes a novel hierarchical approach that leverages Latent Dirichlet Analysis to develop topic models that assist build a high-level Psychosocial feature set we call PDC. PDC organizes words into word families, which helps capture codeswitching during preprocessing for supervised learning models. Informed by the duplex theory of hate, the PDC features are based on a hate speech annotation framework. Frequency-based models employing the PDC feature on tweets from the 2012 and 2017 Kenyan presidential elections yielded an f-score of 83 percent (precision: 81 percent, recall: 85 percent) in recognizing hate speech. The study is notable because it publicly exposes a rich codeswitched dataset for comparative studies. Second, it describes how to create a novel PDC feature set to detect subtle types of hate speech hidden in codeswitched data that previous approaches could not detect.

scholarly journals A Web Interface for Analyzing Hate Speech

2021 ◽  
Vol 13 (3) ◽  
pp. 80
Author(s):  
Lazaros Vrysis ◽  
Nikolaos Vryzas ◽  
Rigas Kotsakis ◽  
Theodora Saridou ◽  
Maria Matsiola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Graphical User Interface ◽  
Hate Speech ◽  
Web Interface ◽  
Learning Models ◽  
Speech Detection ◽  
Media Services ◽  
The Web ◽  
Machine Learning Models

Social media services make it possible for an increasing number of people to express their opinion publicly. In this context, large amounts of hateful comments are published daily. The PHARM project aims at monitoring and modeling hate speech against refugees and migrants in Greece, Italy, and Spain. In this direction, a web interface for the creation and the query of a multi-source database containing hate speech-related content is implemented and evaluated. The selected sources include Twitter, YouTube, and Facebook comments and posts, as well as comments and articles from a selected list of websites. The interface allows users to search in the existing database, scrape social media using keywords, annotate records through a dedicated platform and contribute new content to the database. Furthermore, the functionality for hate speech detection and sentiment analysis of texts is provided, making use of novel methods and machine learning models. The interface can be accessed online with a graphical user interface compatible with modern internet browsers. For the evaluation of the interface, a multifactor questionnaire was formulated, targeting to record the users’ opinions about the web interface and the corresponding functionality.

scholarly journals Building and Annotating a Codeswitched Hate Speech Corpora

2021 ◽  
Vol 13 (3) ◽  
pp. 33-52
Author(s):  
Edward Ombui ◽  
◽  
Lawrence Muchemi ◽  
Peter Wagacha
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Hate Speech ◽  
Empirical Studies ◽  
Presidential Campaign ◽  
Supervised Machine Learning ◽  
Annotation Scheme ◽  
Speech Corpora ◽  
Duplex Theory ◽  
Speech Identification

Presidential campaign periods are a major trigger event for hate speech on social media in almost every country. A systematic review of previous studies indicates inadequate publicly available annotated datasets and hardly any evidence of theoretical underpinning for the annotation schemes used for hate speech identification. This situation stifles the development of empirically useful data for research, especially in supervised machine learning. This paper describes the methodology that was used to develop a multidimensional hate speech framework based on the duplex theory of hate [1] components that include distance, passion, commitment to hate, and hate as a story. Subsequently, an annotation scheme based on the framework was used to annotate a random sample of ~51k tweets from ~400k tweets that were collected during the August and October 2017 presidential campaign period in Kenya. This resulted in a goldstandard codeswitched dataset that could be used for comparative and empirical studies in supervised machine learning. The resulting classifiers trained on this dataset could be used to provide real-time monitoring of hate speech spikes on social media and inform data-driven decision-making by relevant security agencies in government.

scholarly journals Morphological Skip-Gram: Replacing FastText characters n-gram with morphological knowledge

2021 ◽  
Vol 24 (67) ◽  
pp. 1-17
Author(s):  
Flávio Arthur O. Santos ◽  
Thiago Dias Bispo ◽  
Hendrik Teixeira Macedo ◽  
Cleber Zanchettin
Keyword(s):  
Language Processing ◽  
Hate Speech ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Training Phase ◽  
Word Embeddings ◽  
Speech Detection ◽  
Word Representation ◽  
N Gram ◽  
Good Word

Natural language processing systems have attracted much interest of the industry. This branch of study is composed of some applications such as machine translation, sentiment analysis, named entity recognition, question and answer, and others. Word embeddings (i.e., continuous word representations) are an essential module for those applications generally used as word representation to machine learning models. Some popular methods to train word embeddings are GloVe and Word2Vec. They achieve good word representations, despite limitations: both ignore morphological information of the words and consider only one representation vector for each word. This approach implies the word embeddings does not consider different word contexts properly and are unaware of its inner structure. To mitigate this problem, the other word embeddings method FastText represents each word as a bag of characters n-grams. Hence, a continuous vector describes each n-gram, and the final word representation is the sum of its characters n-grams vectors. Nevertheless, the use of all n-grams character of a word is a poor approach since some n-grams have no semantic relation with their words and increase the amount of potentially useless information. This approach also increase the training phase time. In this work, we propose a new method for training word embeddings, and its goal is to replace the FastText bag of character n-grams for a bag of word morphemes through the morphological analysis of the word. Thus, words with similar context and morphemes are represented by vectors close to each other. To evaluate our new approach, we performed intrinsic evaluations considering 15 different tasks, and the results show a competitive performance compared to FastText. Moreover, the proposed model is $40\%$ faster than FastText in the training phase. We also outperform the baseline approaches in extrinsic evaluations through Hate speech detection and NER tasks using different scenarios.

Research Journey of Hate Content Detection From Cyberspace

2021 ◽  
pp. 200-225
Author(s):  
Sayani Ghosal ◽  
Amita Jain
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Digital Media ◽  
Language Processing ◽  
Social Networking Sites ◽  
Hate Speech ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Speech Detection ◽  
Healthy Environment

Hate content detection is the most prospective and challenging research area under the natural language processing domain. Hate speech abuse individuals or groups of people based on religion, caste, language, or sex. Enormous growth of digital media and cyberspace has encouraged researchers to work on hatred speech detection. A commonly acceptable automatic hate detection system is required to stop flowing hate-motivated data. Anonymous hate content is affecting the young generation and adults on social networking sites. Through numerous studies and review papers, the chapter identifies the need for artificial intelligence (AI) in hate speech research. The chapter explores the current state-of-the-art and prospects of AI in natural language processing (NLP) and machine learning algorithms. The chapter aims to identify the most successful methods or techniques for hate speech detection to date. Revolution in this research helps social media to provide a healthy environment for everyone.

scholarly journals Challenges of Hate Speech Detection in Social Media

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
György Kovács ◽  
Pedro Alonso ◽  
Rajkumar Saini
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Hate Speech ◽  
Automatic Detection ◽  
Training Data ◽  
Speech Detection ◽  
Model Combining ◽  
Increased Risk ◽  
Recent Emergence ◽  
Efficient Learning

AbstractThe detection of hate speech in social media is a crucial task. The uncontrolled spread of hate has the potential to gravely damage our society, and severely harm marginalized people or groups. A major arena for spreading hate speech online is social media. This significantly contributes to the difficulty of automatic detection, as social media posts include paralinguistic signals (e.g. emoticons, and hashtags), and their linguistic content contains plenty of poorly written text. Another difficulty is presented by the context-dependent nature of the task, and the lack of consensus on what constitutes as hate speech, which makes the task difficult even for humans. This makes the task of creating large labeled corpora difficult, and resource consuming. The problem posed by ungrammatical text has been largely mitigated by the recent emergence of deep neural network (DNN) architectures that have the capacity to efficiently learn various features. For this reason, we proposed a deep natural language processing (NLP) model—combining convolutional and recurrent layers—for the automatic detection of hate speech in social media data. We have applied our model on the HASOC2019 corpus, and attained a macro F1 score of 0.63 in hate speech detection on the test set of HASOC. The capacity of DNNs for efficient learning, however, also means an increased risk of overfitting. Particularly, with limited training data available (as was the case for HASOC). For this reason, we investigated different methods for expanding resources used. We have explored various opportunities, such as leveraging unlabeled data, similarly labeled corpora, as well as the use of novel models. Our results showed that by doing so, it was possible to significantly increase the classification score attained.

scholarly journals Towards generalisable hate speech detection: a review on obstacles and solutions

2021 ◽  
Vol 7 ◽  
pp. e598
Author(s):  
Wenjie Yin ◽  
Arkaitz Zubiaga
Keyword(s):  
Natural Language Processing ◽  
Sexual Orientation ◽  
Language Processing ◽  
Hate Speech ◽  
Future Research ◽  
Individual Member ◽  
Speech Detection ◽  
Survey Paper ◽  
Unseen Data ◽  
Natural Language Processing Task

Hate speech is one type of harmful online content which directly attacks or promotes hate towards a group or an individual member based on their actual or perceived aspects of identity, such as ethnicity, religion, and sexual orientation. With online hate speech on the rise, its automatic detection as a natural language processing task is gaining increasing interest. However, it is only recently that it has been shown that existing models generalise poorly to unseen data. This survey paper attempts to summarise how generalisable existing hate speech detection models are and the reasons why hate speech models struggle to generalise, sums up existing attempts at addressing the main obstacles, and then proposes directions of future research to improve generalisation in hate speech detection.

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