The past several years have witnessed a huge surge in the use of social media
platforms during mass convergence events such as health emergencies, natural or
human-induced disasters. These non-traditional data sources are becoming vital
for disease forecasts and surveillance when preparing for epidemic and pandemic
outbreaks. In this paper, we present GeoCoV19, a large-scale Twitter dataset
containing more than 524 million multilingual tweets posted over a period of 90
days since February 1, 2020. Moreover, we employ a gazetteer-based approach to
infer the geolocation of tweets. We postulate that this large-scale,
multilingual, geolocated social media data can empower the research communities
to evaluate how societies are collectively coping with this unprecedented
global crisis as well as to develop computational methods to address challenges
such as identifying fake news, understanding communities' knowledge gaps,
building disease forecast and surveillance models, among others.

该文介绍了 GeoCoV19—— 一个包含 524 百万条推文的大规模 Twitter 数据集，通过基于地名词典的方法来推断推文的地理位置，而这一大规模、多语言、地理定位的社交媒体数据可以帮助研究社区评估社会如何共同应对这一前所未有的全球危机，以及建立计算方法来应对如识别假新闻，理解社区的知识差距，建立疾病预测和监测模型等挑战。

GeoCoV19: 一个拥有数亿条多语言 COVID-19 推文和位置信息的数据集

GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19  Tweets with Location Information

Identifying misinformation is increasingly being recognized as an important
computational task with high potential social impact. Misinformation and fake
contents are injected into almost every domain of news including politics,
health, science, business, etc., among which, the fakeness in health domain
pose serious adverse effects to scare or harm the society. Misinformation
contains scientific claims or content from social media exaggerated with strong
emotion content to attract eyeballs. In this paper, we consider the utility of
the affective character of news articles for fake news identification in the
health domain and present evidence that emotion cognizant representations are
significantly more suited for the task. We outline a technique to leverage
emotion intensity lexicons to develop emotionized text representations, and
evaluate the utility of such a representation for identifying fake news
relating to health in various supervised and unsupervised scenarios. The
consistent and significant empirical gains that we observe over a range of
technique types and parameter settings establish the utility of the emotional
information in news articles, an often overlooked aspect, for the task of
misinformation identification in the health domain.

本研究考虑了新闻文章情感信息对健康领域虚假新闻识别的作用，利用情感强度词库开发情感化文本表示技术，在多种监督和无监督情况下评估了该表示技术的实用性，结果表明情感信息的使用可以显著提高健康领域识别虚假信息的准确率。

情感认知提高健康虚假新闻辨别

Emotion Cognizance Improves Health Fake News Identification

Identifying the veracity of a news article is an interesting problem while
automating this process can be a challenging task. Detection of a news article
as fake is still an open question as it is contingent on many factors which the
current state-of-the-art models fail to incorporate. In this paper, we explore
a subtask to fake news identification, and that is stance detection. Given a
news article, the task is to determine the relevance of the body and its claim.
We present a novel idea that combines the neural, statistical and external
features to provide an efficient solution to this problem. We compute the
neural embedding from the deep recurrent model, statistical features from the
weighted n-gram bag-of-words model and handcrafted external features with the
help of feature engineering heuristics. Finally, using deep neural layer all
the features are combined, thereby classifying the headline-body news pair as
agree, disagree, discuss, or unrelated. We compare our proposed technique with
the current state-of-the-art models on the fake news challenge dataset. Through
extensive experiments, we find that the proposed model outperforms all the
state-of-the-art techniques including the submissions to the fake news
challenge.

本文探讨假新闻鉴别的子任务，即立场检测，并提出了一种新的思路，将神经网络、统计特征和外部特征结合起来，通过深度神经网络将这些特征组合在一起，最终对新闻标题 - 正文进行分类，并在假新闻挑战数据集上进行了实验，结果表明该模型胜于现有的所有技术。