APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversations

Using style-transfer models to reduce offensiveness of social media comments can help foster a more inclusive environment. However, there are no sizable datasets that contain offensive texts and their inoffensive counterparts, and fine-tuning pretrained models with limited labeled data can lead to the loss of original meaning in the style-transferred text. To address this issue, we provide two major contributions. First, we release the first publicly-available, parallel corpus of offensive Reddit comments and their style-transferred counterparts annotated by expert sociolinguists. Then, we introduce the first discourse-aware style-transfer models that can effectively reduce offensiveness in Reddit text while preserving the meaning of the original text. These models are the first to examine inferential links between the comment and the text it is replying to when transferring the style of offensive Reddit text. We propose two different methods of integrating discourse relations with pretrained transformer models and evaluate them on our dataset of offensive comments from Reddit and their inoffensive counterparts. Improvements over the baseline with respect to both automatic metrics and human evaluation indicate that our discourse-aware models are better at preserving meaning in style-transferred text when compared to the state-of-the-art discourse-agnostic models.Comment: To be published in Proceedings of COLING 2022, the 29th International Conference on Computational Linguistic

Text Style Transfer: A Review and Experimental Evaluation

The stylistic properties of text have intrigued computational linguistics researchers in recent years. Specifically, researchers have investigated the Text Style Transfer (TST) task, which aims to change the stylistic properties of the text while retaining its style independent content. Over the last few years, many novel TST algorithms have been developed, while the industry has leveraged these algorithms to enable exciting TST applications. The field of TST research has burgeoned because of this symbiosis. This article aims to provide a comprehensive review of recent research efforts on text style transfer. More concretely, we create a taxonomy to organize the TST models and provide a comprehensive summary of the state of the art. We review the existing evaluation methodologies for TST tasks and conduct a large-scale reproducibility study where we experimentally benchmark 19 state-of-the-art TST algorithms on two publicly available datasets. Finally, we expand on current trends and provide new perspectives on the new and exciting developments in the TST field

A Framework for Privacy and Hate Speeches Control on Social Media in Mobile Computing

The hype in information technology has led to the use of mobile devices to carry out several activities such as financial transactions, communications, learning and other related activities using the internet. Social networking platforms today are used to facilitate the activities carried out on mobile devices. The use of these social network platforms such as Facebook, Twitter, Whatsapp, Snapchat, Instagram among others on mobile devices is called mobile social network. A mobile social network makes it faster and easier for users to relate to one another and carry out other related activities at convenience. The major challenge with the use of social network applications on mobile devices is that, many illegal activities are carried out such as hate speeches and bigotry statement which has demeaning the interest of users. This research work proposes a technique for discovering and blocking hate speeches and bigotry users on social network platforms using text scanning and matching algorithm; in order to curtail the damages caused to national security and unity by such statement. Keywords: Mobile social network, social network