10 research outputs found
Learning Hierarchical Discourse-level Structure for Fake News Detection
On the one hand, nowadays, fake news articles are easily propagated through
various online media platforms and have become a grand threat to the
trustworthiness of information. On the other hand, our understanding of the
language of fake news is still minimal. Incorporating hierarchical
discourse-level structure of fake and real news articles is one crucial step
toward a better understanding of how these articles are structured.
Nevertheless, this has rarely been investigated in the fake news detection
domain and faces tremendous challenges. First, existing methods for capturing
discourse-level structure rely on annotated corpora which are not available for
fake news datasets. Second, how to extract out useful information from such
discovered structures is another challenge. To address these challenges, we
propose Hierarchical Discourse-level Structure for Fake news detection. HDSF
learns and constructs a discourse-level structure for fake/real news articles
in an automated and data-driven manner. Moreover, we identify insightful
structure-related properties, which can explain the discovered structures and
boost our understating of fake news. Conducted experiments show the
effectiveness of the proposed approach. Further structural analysis suggests
that real and fake news present substantial differences in the hierarchical
discourse-level structures.Comment: Accepted to 2019 Annual Conference of the North American Chapter of
the Association for Computational Linguistics June 2-7, 2019 Minneapolis, US
Exploring Thematic Coherence in Fake News
The spread of fake news remains a serious global issue; understanding and
curtailing it is paramount. One way of differentiating between deceptive and
truthful stories is by analyzing their coherence. This study explores the use
of topic models to analyze the coherence of cross-domain news shared online.
Experimental results on seven cross-domain datasets demonstrate that fake news
shows a greater thematic deviation between its opening sentences and its
remainder.Comment: 10 pages, 1 figure, to be published in Proceedings of the 8th
International Workshop on News Recommendation and Analytics (INRA 2020
SAFE: Similarity-Aware Multi-Modal Fake News Detection
Effective detection of fake news has recently attracted significant
attention. Current studies have made significant contributions to predicting
fake news with less focus on exploiting the relationship (similarity) between
the textual and visual information in news articles. Attaching importance to
such similarity helps identify fake news stories that, for example, attempt to
use irrelevant images to attract readers' attention. In this work, we propose a
imilarity-ware ak news
detection method () which investigates multi-modal (textual and
visual) information of news articles. First, neural networks are adopted to
separately extract textual and visual features for news representation. We
further investigate the relationship between the extracted features across
modalities. Such representations of news textual and visual information along
with their relationship are jointly learned and used to predict fake news. The
proposed method facilitates recognizing the falsity of news articles based on
their text, images, or their "mismatches." We conduct extensive experiments on
large-scale real-world data, which demonstrate the effectiveness of the
proposed method.Comment: To be published in The 24th Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD 2020
Fake News Detection using Temporal Features Extracted via Point Process
Many people use social networking services (SNSs) to easily access various
news. There are numerous ways to obtain and share ``fake news,'' which are news
carrying false information. To address fake news, several studies have been
conducted for detecting fake news by using SNS-extracted features. In this
study, we attempt to use temporal features generated from SNS posts by using a
point process algorithm to identify fake news from real news. Temporal features
in fake news detection have the advantage of robustness over existing features
because it has minimal dependence on fake news propagators. Further, we propose
a novel multi-modal attention-based method, which includes linguistic and user
features alongside temporal features, for detecting fake news from SNS posts.
Results obtained from three public datasets indicate that the proposed model
achieves better performance compared to existing methods and demonstrate the
effectiveness of temporal features for fake news detection.Comment: CySoc 2020 International Workshop on Cyber Social Threats, ICWSM 202
Unleashing the Power of Neural Discourse Parsers -- A Context and Structure Aware Approach Using Large Scale Pretraining
RST-based discourse parsing is an important NLP task with numerous downstream
applications, such as summarization, machine translation and opinion mining. In
this paper, we demonstrate a simple, yet highly accurate discourse parser,
incorporating recent contextual language models. Our parser establishes the new
state-of-the-art (SOTA) performance for predicting structure and nuclearity on
two key RST datasets, RST-DT and Instr-DT. We further demonstrate that
pretraining our parser on the recently available large-scale "silver-standard"
discourse treebank MEGA-DT provides even larger performance benefits,
suggesting a novel and promising research direction in the field of discourse
analysis.Comment: 10 pages, 1 figure, COLING 202
From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation
Sentiment analysis, especially for long documents, plausibly requires methods
capturing complex linguistics structures. To accommodate this, we propose a
novel framework to exploit task-related discourse for the task of sentiment
analysis. More specifically, we are combining the large-scale,
sentiment-dependent MEGA-DT treebank with a novel neural architecture for
sentiment prediction, based on a hybrid TreeLSTM hierarchical attention model.
Experiments show that our framework using sentiment-related discourse
augmentations for sentiment prediction enhances the overall performance for
long documents, even beyond previous approaches using well-established
discourse parsers trained on human annotated data. We show that a simple
ensemble approach can further enhance performance by selectively using
discourse, depending on the document length.Comment: In Proceedings of the 28 International Conference on Computational
Linguistics (COLING). 10 page
Credibility-based Fake News Detection
Fake news can significantly misinform people who often rely on online sources
and social media for their information. Current research on fake news detection
has mostly focused on analyzing fake news content and how it propagates on a
network of users. In this paper, we emphasize the detection of fake news by
assessing its credibility. By analyzing public fake news data, we show that
information on news sources (and authors) can be a strong indicator of
credibility. Our findings suggest that an author's history of association with
fake news, and the number of authors of a news article, can play a significant
role in detecting fake news. Our approach can help improve traditional fake
news detection methods, wherein content features are often used to detect fake
news
Network-based Fake News Detection: A Pattern-driven Approach
Fake news gains has gained significant momentum, strongly motivating the need
for fake news research. Many fake news detection approaches have thus been
proposed, where most of them heavily rely on news content. However,
network-based clues revealed when analyzing news propagation on social networks
is an information that has hardly been comprehensively explored or used for
fake news detection. We bridge this gap by proposing a network-based
pattern-driven fake news detection approach. We aim to study the patterns of
fake news in social networks, which refer to the news being spread, spreaders
of the news and relationships among the spreaders. Empirical evidence and
interpretations on the existence of such patterns are provided based on social
psychological theories. These patterns are then represented at various network
levels (i.e., node-level, ego-level, triad-level, community-level and the
overall network) for being further utilized to detect fake news. The proposed
approach enhances the explainability in fake news feature engineering.
Experiments conducted on real-world data demonstrate that the proposed approach
can outperform the state of the arts
Characterizing the Decision Boundary of Deep Neural Networks
Deep neural networks and in particular, deep neural classifiers have become
an integral part of many modern applications. Despite their practical success,
we still have limited knowledge of how they work and the demand for such an
understanding is evergrowing. In this regard, one crucial aspect of deep neural
network classifiers that can help us deepen our knowledge about their
decision-making behavior is to investigate their decision boundaries.
Nevertheless, this is contingent upon having access to samples populating the
areas near the decision boundary. To achieve this, we propose a novel approach
we call Deep Decision boundary Instance Generation (DeepDIG). DeepDIG utilizes
a method based on adversarial example generation as an effective way of
generating samples near the decision boundary of any deep neural network model.
Then, we introduce a set of important principled characteristics that take
advantage of the generated instances near the decision boundary to provide
multifaceted understandings of deep neural networks. We have performed
extensive experiments on multiple representative datasets across various deep
neural network models and characterized their decision boundaries. The code is
publicly available at https://github.com/hamidkarimi/DeepDIG/.Comment: Please contact the first author for any issue or the question
regarding this pape
Disinformation, Misinformation, and Fake News in Social Media [electronic resource] : Emerging Research Challenges and Opportunities /
This book serves as a convenient entry point for researchers, practitioners, and students to understand the problems and challenges, learn state-of-the-art solutions for their specific needs, and quickly identify new research problems in their domains. The contributors to this volume describe the recent advancements in three related parts: (1) user engagements in the dissemination of information disorder; (2) techniques on detecting and mitigating disinformation; and (3) trending issues such as ethics, blockchain, clickbaits, etc. This edited volume will appeal to students, researchers, and professionals working on disinformation, misinformation and fake news in social media from a unique lens.A Social Network Analysis and Cyber Forensics Informed Exploration of Disinformation Campaigns -- Mitigating Fake News through Fact-checking URL recommendation -- Detecting Fake News with Semi-supervised Tensor Decomposition -- Learning Hierarchical Discourse-level Structure for Fake News Detection -- Mining Styles and Emotions for Fake News Detection -- Fake News Detection with Deep Diffusive Network Model -- Fake News Detection: An Interdisciplinary Research -- Jointly Identifying Framing Bias and Detecting Fake News on Social Media.This book serves as a convenient entry point for researchers, practitioners, and students to understand the problems and challenges, learn state-of-the-art solutions for their specific needs, and quickly identify new research problems in their domains. The contributors to this volume describe the recent advancements in three related parts: (1) user engagements in the dissemination of information disorder; (2) techniques on detecting and mitigating disinformation; and (3) trending issues such as ethics, blockchain, clickbaits, etc. This edited volume will appeal to students, researchers, and professionals working on disinformation, misinformation and fake news in social media from a unique lens