Jisun An


2020

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What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context
Ramy Baly | Georgi Karadzhov | Jisun An | Haewoon Kwak | Yoan Dinkov | Ahmed Ali | James Glass | Preslav Nakov
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Predicting the political bias and the factuality of reporting of entire news outlets are critical elements of media profiling, which is an understudied but an increasingly important research direction. The present level of proliferation of fake, biased, and propagandistic content online has made it impossible to fact-check every single suspicious claim, either manually or automatically. Thus, it has been proposed to profile entire news outlets and to look for those that are likely to publish fake or biased content. This makes it possible to detect likely “fake news” the moment they are published, by simply checking the reliability of their source. From a practical perspective, political bias and factuality of reporting have a linguistic aspect but also a social context. Here, we study the impact of both, namely (i) what was written (i.e., what was published by the target medium, and how it describes itself in Twitter) vs. (ii) who reads it (i.e., analyzing the target medium’s audience on social media). We further study (iii) what was written about the target medium (in Wikipedia). The evaluation results show that what was written matters most, and we further show that putting all information sources together yields huge improvements over the current state-of-the-art.

2019

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Tanbih: Get To Know What You Are Reading
Yifan Zhang | Giovanni Da San Martino | Alberto Barrón-Cedeño | Salvatore Romeo | Jisun An | Haewoon Kwak | Todor Staykovski | Israa Jaradat | Georgi Karadzhov | Ramy Baly | Kareem Darwish | James Glass | Preslav Nakov
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what’s behind a news story. Our system displays news grouped into events and generates media profiles that show the general factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame of reporting, and stance with respect to various claims and topics of a news outlet. In addition, we automatically analyse each article to detect whether it is propagandistic and to determine its stance with respect to a number of controversial topics.

2018

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SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment
Jisun An | Haewoon Kwak | Yong-Yeol Ahn
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Because word semantics can substantially change across communities and contexts, capturing domain-specific word semantics is an important challenge. Here, we propose SemAxis, a simple yet powerful framework to characterize word semantics using many semantic axes in word-vector spaces beyond sentiment. We demonstrate that SemAxis can capture nuanced semantic representations in multiple online communities. We also show that, when the sentiment axis is examined, SemAxis outperforms the state-of-the-art approaches in building domain-specific sentiment lexicons.