A probabilistic clustering model for hate speech classification in twitter

Ayo, Femi Emmanuel; Folorunso, Olusegun; Ibharalu, F. T.; Osinuga, Idowu Ademola; Abayomi‐Alli, Adebayo

doi:10.1016/j.eswa.2021.114762

Cited by 42 publications

(19 citation statements)

References 51 publications

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“…The first step is to incorporate the Twitter data upon which a comprehensive pre-processing method has been carried out, afterwards extraction of features from the resulting pre-processed tweets has been accomplished. Finally, proposed ensemble methods have been introduced to predict if any unknown test tweet is hateful or nonhateful [29], which means the final decisional outcome is produced based on the ensemble classifiers' output [35][36][37][38][39][40][41][42]. For each tweet we performed the following:…”

Section: Dataset Preparation and Preprocessingmentioning

confidence: 99%

Novel Hate Speech Detection Using Word Cloud Visualization and Ensemble Learning Coupled with Count Vectorizer

Turki

Roy

2022

Applied Sciences

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A plethora of negative behavioural activities have recently been found in social media. Incidents such as trolling and hate speech on social media, especially on Twitter, have grown considerably. Therefore, detection of hate speech on Twitter has become an area of interest among many researchers. In this paper, we present a computational framework to (1) examine out the computational challenges behind hate speech detection and (2) generate high performance results. First, we extract features from Twitter data by utilizing a count vectorizer technique. Then, we provide the labeled dataset of constructed features to adopted ensemble methods, including Bagging, AdaBoost, and Random Forest. After training, we classify new tweet examples into one of the two categories, hate speech or non-hate speech. Experimental results show (1) that Random Forest has surpassed other methods by generating 95% using accuracy performance results and (2) word cloud displays the most prominent tweets that are responsible for hateful sentiments.

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Section: Dataset Preparation and Preprocessingmentioning

confidence: 99%

Novel Hate Speech Detection Using Word Cloud Visualization and Ensemble Learning Coupled with Count Vectorizer

Turki

Roy

2022

Applied Sciences

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“…Topic classification for social media aims to detect and track general topics such as “Baseball” or “Fashion”. In previous work, researchers have collected labeled data either by using a single hashtag for each topic ( Lin, Snow & Morgan, 2011 ), a user-defined query for each topic ( Magdy & Elsayed, 2014 ), manual labeling ( Daouadi, Zghal Rebaï & Amous, 2021 ; Ayo et al, 2021 ), or co-training based on the URLs and text of the tweet ( Yang et al, 2014 ). We expand on Lin, Snow & Morgan (2011) ’s work and use a set of hashtags instead of a single hashtag.…”

Section: Related Workmentioning

confidence: 99%

A longitudinal study of topic classification on Twitter

Iman¹,

Sanner²,

Bouadjenek³

et al. 2022

PeerJ Computer Science

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Twitter represents a massively distributed information source over topics ranging from social and political events to entertainment and sports news. While recent work has suggested this content can be narrowed down to the personalized interests of individual users by training topic filters using standard classifiers, there remain many open questions about the efficacy of such classification-based filtering approaches. For example, over a year or more after training, how well do such classifiers generalize to future novel topical content, and are such results stable across a range of topics? In addition, how robust is a topic classifier over the time horizon, e.g., can a model trained in 1 year be used for making predictions in the subsequent year? Furthermore, what features, feature classes, and feature attributes are most critical for long-term classifier performance? To answer these questions, we collected a corpus of over 800 million English Tweets via the Twitter streaming API during 2013 and 2014 and learned topic classifiers for 10 diverse themes ranging from social issues to celebrity deaths to the “Iran nuclear deal”. The results of this long-term study of topic classifier performance provide a number of important insights, among them that: (i) such classifiers can indeed generalize to novel topical content with high precision over a year or more after training though performance degrades with time, (ii) the classes of hashtags and simple terms contain the most informative feature instances, (iii) removing tweets containing training hashtags from the validation set allows better generalization, and (iv) the simple volume of tweets by a user correlates more with their informativeness than their follower or friend count. In summary, this work provides a long-term study of topic classifiers on Twitter that further justifies classification-based topical filtering approaches while providing detailed insight into the feature properties most critical for topic classifier performance.

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“…One step to make this society more secure is detecting and removing these duplicate profiles. These profiles can induce unethical thinking or activities, such as sexist ideologies [8]. Sometimes, the aim is to detect specific topics in real-time [9].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Similarity Measurement of Twitter Profiles

Shoeibi¹,

Shoeibi²,

Chamoso³

et al. 2022

Preprint

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Social media platforms have been entirely an undeniable part of the lifestyle for the past decade. Analyzing the information being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and risen user satisfaction. For deriving any further conclusion, first, it is necessary to know how to compare users. In this paper, a hybrid model has been proposed to measure Twitter profiles’ similarity and quantifies the likeness degree of profiles by calculating features considering users’ behavioral habits. For this, first, the timeline of each profile has been extracted using the official TwitterAPI. Then, in parallel, three aspects of a profile are deliberated. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized to compare the behavioral ratios of two profiles. Next, the audience network is extracted for each user, and for estimating the similarity of two sets, Jaccard similarity is used. Finally, for the Content similarity measurement, the tweets are preprocessed respecting the feature extraction method; TF-IDF and DistilBERT for feature extraction are employed and then compared using the cosine similarity method. Results have shown that TF-IDF has slightly better performance; therefore, the more straightforward solution is selected for the model. Similarity level of different profiles. As in the case study, a Random Forest classification model was trained on almost 20000 users revealed a 97.24% accuracy. This comparison enables us to find duplicate profiles with nearly the same behavior and content.

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A probabilistic clustering model for hate speech classification in twitter

Cited by 42 publications

References 51 publications

Novel Hate Speech Detection Using Word Cloud Visualization and Ensemble Learning Coupled with Count Vectorizer

Novel Hate Speech Detection Using Word Cloud Visualization and Ensemble Learning Coupled with Count Vectorizer

A longitudinal study of topic classification on Twitter

A Hybrid Model for Similarity Measurement of Twitter Profiles

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