39 research outputs found
Automated Detection of Bilingual Obfuscated Abusive Words on Social Media Forums: A Case of Swahili and English Texts
The usage of social media has exponentially grown in recent years leaving the users with no limitations on misusing the platforms through abusive contents as deemed fit to them. This exacerbates abusive words exposure to innocent users, especially in social media forums, including children. In an attempt to alleviate the problem of abusive words proliferation on social media, researchers have proposed different methods to help deal with variants of the abusive words; however, obfuscated abusive words detection still poses challenges. A method that utilizes a combination of rule based approach and character percentage matching techniques is proposed to improve the detection rate for obfuscated abusive words. The evaluation results achieved F1 score percentage ratio of 0.97 and accuracy percentage ratio of 0.96 which were above the significance ratio of 0.5. Hence, the proposed approach is highly effective for obfuscated abusive words detection and prevention.
Keywords: Rule based approach, Character percentage matching techniques, Obfuscated abuse, Abuse detection, Abusive words, Social medi
Abusive Text Detection Using Neural Networks
eural network models have become increasingly popular for text classification in recent years. In particular, the emergence of word embeddings within deep learning architectures has recently attracted a high level of attention amongst researchers. In this paper, we focus on how neural network models have been applied in text classification. Secondly, we extend our previous work [4, 3] using a neural network strategy for the task of abusive text detection. We compare word embedding features to the traditional feature representations such as n-grams and handcrafted features. In addition, we use an off-the-shelf neural network classifier, FastText[16]. Based on our results, the conclusions are: (1) Extracting selected manual features can increase abusive content detection over using basic ngrams; (2) Although averaging pre-trained word embeddings is a naive method, the distributed feature representation has better performance to ngrams in most of our datasets; (3) While the FastText classifier works efficiently with fast performance, the results are not remarkable as it is a shallow neural network with only one hidden layer; (4) Using pre-trained word embeddings does not guarantee better performance in the FastText classifie
Offensive Language Identification in Greek
As offensive language has become a rising issue for online communities and
social media platforms, researchers have been investigating ways of coping with
abusive content and developing systems to detect its different types:
cyberbullying, hate speech, aggression, etc. With a few notable exceptions,
most research on this topic so far has dealt with English. This is mostly due
to the availability of language resources for English. To address this
shortcoming, this paper presents the first Greek annotated dataset for
offensive language identification: the Offensive Greek Tweet Dataset (OGTD).
OGTD is a manually annotated dataset containing 4,779 posts from Twitter
annotated as offensive and not offensive. Along with a detailed description of
the dataset, we evaluate several computational models trained and tested on
this data.Comment: Accepted to LREC 202
Understanding the Bystander Effect on Toxic Twitter Conversations
In this study, we explore the power of group dynamics to shape the toxicity
of Twitter conversations. First, we examine how the presence of others in a
conversation can potentially diffuse Twitter users' responsibility to address a
toxic direct reply. Second, we examine whether the toxicity of the first direct
reply to a toxic tweet in conversations establishes the group norms for
subsequent replies. By doing so, we outline how bystanders and the tone of
initial responses to a toxic reply are explanatory factors which affect whether
others feel uninhibited to post their own abusive or derogatory replies. We
test this premise by analyzing a random sample of more than 156k tweets
belonging to ~9k conversations. Central to this work is the social
psychological research on the "bystander effect" documenting that the presence
of bystanders has the power to alter the dynamics of a social situation. If the
first direct reply reaffirms the divisive tone, other replies may follow suit.
We find evidence of a bystander effect, with our results showing that an
increased number of users participating in the conversation before receiving a
toxic tweet is negatively associated with the number of Twitter users who
responded to the toxic reply in a non-toxic way. We also find that the initial
responses to toxic tweets within conversations is of great importance. Posting
a toxic reply immediately after a toxic comment is negatively associated with
users posting non-toxic replies and Twitter conversations becoming increasingly
toxic
Abusive Text Detection Using Neural Networks
Neurall network models have become increasingly popular for text classification in recent years. In particular, the emergence of word embeddings within deep learning architecture has recently attracted a high level of attention amongst researchers