Piush Aggarwal


Analyzing the Real Vulnerability of Hate Speech Detection Systems against Targeted Intentional Noise
Piush Aggarwal | Torsten Zesch
Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)

Hate speech detection systems have been shown to be vulnerable against obfuscation attacks, where a potential hater tries to circumvent detection by deliberately introducing noise in their posts. In previous work, noise is often introduced for all words (which is likely overestimating the impact) or single untargeted words (likely underestimating the vulnerability). We perform a user study asking people to select words they would obfuscate in a post. Using this realistic setting, we find that the real vulnerability of hate speech detection systems against deliberately introduced noise is almost as high as when using a whitebox attack and much more severe than when using a non-targeted dictionary. Our results are based on 4 different datasets, 12 different obfuscation strategies, and hate speech detection systems using different paradigms.

Bye, Bye, Maintenance Work? Using Model Cloning to Approximate the Behavior of Legacy Tools
Piush Aggarwal | Torsten Zesch
Proceedings of the 18th Conference on Natural Language Processing (KONVENS 2022)


VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes
Piush Aggarwal | Michelle Espranita Liman | Darina Gold | Torsten Zesch
Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021. We build our system on top of a state-of-the-art system for binary hateful meme classification that already uses image tags such as race, gender, and web entities. We add further metadata such as emotions and experiment with data augmentation techniques, as hateful instances are underrepresented in the data set.


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Identification of Good and Bad News on Twitter
Piush Aggarwal | Ahmet Aker
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Social media plays a great role in news dissemination which includes good and bad news. However, studies show that news, in general, has a significant impact on our mental stature and that this influence is more in bad news. An ideal situation would be that we have a tool that can help to filter out the type of news we do not want to consume. In this paper, we provide the basis for such a tool. In our work, we focus on Twitter. We release a manually annotated dataset containing 6,853 tweets from 5 different topical categories. Each tweet is annotated with good and bad labels. We also investigate various machine learning systems and features and evaluate their performance on the newly generated dataset. We also perform a comparative analysis with sentiments showing that sentiment alone is not enough to distinguish between good and bad news.

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Classification Approaches to Identify Informative Tweets
Piush Aggarwal
Proceedings of the Student Research Workshop Associated with RANLP 2019

Social media platforms have become prime forums for reporting news, with users sharing what they saw, heard or read on social media. News from social media is potentially useful for various stakeholders including aid organizations, news agencies, and individuals. However, social media also contains a vast amount of non-news content. For users to be able to draw on benefits from news reported on social media it is necessary to reliably identify news content and differentiate it from non-news. In this paper, we tackle the challenge of classifying a social post as news or not. To this end, we provide a new manually annotated dataset containing 2,992 tweets from 5 different topical categories. Unlike earlier datasets, it includes postings posted by personal users who do not promote a business or a product and are not affiliated with any organization. We also investigate various baseline systems and evaluate their performance on the newly generated dataset. Our results show that the best classifiers are the SVM and BERT models.

LTL-UDE at SemEval-2019 Task 6: BERT and Two-Vote Classification for Categorizing Offensiveness
Piush Aggarwal | Tobias Horsmann | Michael Wojatzki | Torsten Zesch
Proceedings of the 13th International Workshop on Semantic Evaluation

We present results for Subtask A and C of SemEval 2019 Shared Task 6. In Subtask A, we experiment with an embedding representation of postings and use BERT to categorize postings. Our best result reaches the 10th place (out of 103). In Subtask C, we applied a two-vote classification approach with minority fallback, which is placed on the 19th rank (out of 65).


Information Nutrition Labels: A Plugin for Online News Evaluation
Vincentius Kevin | Birte Högden | Claudia Schwenger | Ali Şahan | Neelu Madan | Piush Aggarwal | Anusha Bangaru | Farid Muradov | Ahmet Aker
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

In this paper we present a browser plugin NewsScan that assists online news readers in evaluating the quality of online content they read by providing information nutrition labels for online news articles. In analogy to groceries, where nutrition labels help consumers make choices that they consider best for themselves, information nutrition labels tag online news articles with data that help readers judge the articles they engage with. This paper discusses the choice of the labels, their implementation and visualization.