Paying Attention to Deflections: Mining Pragmatic Nuances for Whataboutism Detection in Online Discourse

Whataboutism, a potent tool for disrupting narratives and sowing distrust, remains under-explored in quantitative NLP research. Moreover, past work has not distinguished its use as a strategy for misinformation and propaganda from its use as a tool for pragmatic and semantic framing. We introduce new datasets from Twitter and YouTube, revealing overlaps as well as distinctions between whataboutism, propaganda, and the tu quoque fallacy. Furthermore, drawing on recent work in linguistic semantics, we differentiate the `what about' lexical construct from whataboutism. Our experiments bring to light unique challenges in its accurate detection, prompting the introduction of a novel method using attention weights for negative sample mining. We report significant improvements of 4% and 10% over previous state-of-the-art methods in our Twitter and YouTube collections, respectively.Comment: 14 pages, 5 figure

İki taraflı sıralama problemine spark çerçevesinde gizliliği koruyan bir çözüm

Cataloged from PDF version of article.Thesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2017.Includes bibliographical references (leaves 50-54).The bipartite ranking problem is defined as finding a function that ranks positive instances in a dataset higher than the negative ones. Financial and medical domains are some of the common application areas of the ranking algorithms. However, a common concern for such domains is the privacy of individuals or companies in the dataset. That is, a researcher who wants to discover knowledge from a dataset extracted from such a domain, needs to access the records of all individuals in the dataset in order to run a ranking algorithm. This privacy concern puts limitations on the use of sensitive personal data for such analysis. We propose an efficient solution for the privacy-preserving bipartite ranking problem, where the researcher does not need the raw data of the instances in order to learn a ranking model from the data. The RIMARC (Ranking Instances by Maximizing Area under the ROC Curve) algorithm solves the bipartite ranking problem by learning a model to rank instances. As part of the model, it learns a weight for each feature by analyzing the area under receiver operating characteristic (ROC) curve. RIMARC algorithm is shown to be more accurate and efficient than its counterparts. Thus, we use this algorithm as a building-block and provide a privacy-preserving version of the RIMARC algorithm using homomorphic encryption and secure multi-party computation. In order to increase the time efficiency for big datasets, we have implemented privacy-preserving RIMARC algorithm on Apache Spark, which is a popular parallelization framework with its revolutionary programming paradigm called Resilient Distributed Datasets. Our proposed algorithm lets a data owner outsource the storage and processing of its encrypted dataset to a semi-trusted cloud. Then, a researcher can get the results of his/her queries (to learn the ranking function) on the dataset by interacting with the cloud. During this process, neither the researcher nor the cloud can access any information about the raw dataset. We prove the security of the proposed algorithm and show its efficiency via experiments on real data.by Noushin Salek Faramarzi.M.S