Deep Neural Attention for Misinformation and Deception Detection

PhD thesis in Information technologyAt present the influence of social media on society is so much that without it life seems to have no meaning for many. This kind of over-reliance on social media gives an opportunity to the anarchic elements to take undue advantage. Online misinformation and deception are vivid examples of such phenomenon. The misinformation or fake news spreads faster and wider than the true news [32]. The need of the hour is to identify and curb the spread of misinformation and misleading content automatically at the earliest. Several machine learning models have been proposed by the researchers to detect and prevent misinformation and deceptive content. However, these prior works suffer from some limitations: First, they either use feature engineering heavy methods or use intricate deep neural architectures, which are not so transparent in terms of their internal working and decision making. Second, they do not incorporate and learn the available auxiliary and latent cues and patterns, which can be very useful in forming the adequate context for the misinformation. Third, Most of the former methods perform poorly in early detection accuracy measures because of their reliance on features that are usually absent at the initial stage of news or social media posts on social networks. In this dissertation, we propose suitable deep neural attention based solutions to overcome these limitations. For instance, we propose a claim verification model, which learns embddings for the latent aspects such as author and subject of the claim and domain of the external evidence document. This enables the model to learn important additional context other than the textual content. In addition, we also propose an algorithm to extract evidential snippets out of external evidence documents, which serves as explanation of the model’s decisions. Next, we improve this model by using improved claim driven attention mechanism and also generate a topically diverse and non-redundant multi-document fact-checking summary for the claims, which helps to further interpret the model’s decision making. Subsequently, we introduce a novel method to learn influence and affinity relationships among the social media users present on the propagation paths of the news items. By modeling the complex influence relationship among the users, in addition to textual content, we learn the significant patterns pertaining to the diffusion of the news item on social network. The evaluation shows that the proposed model outperforms the other related methods in early detection performance with significant gains. Next, we propose a synthetic headline generation based headline incongruence detection model. Which uses a word-to-word mutual attention based deep semantic matching between original and synthetic news headline to detect incongruence. Further, we investigate and define a new task of incongruence detection in presence of important cardinal values in headline. For this new task, we propose a part-of-speech pattern driven attention based method, which learns requisite context for cardinal values

MuSeM: Detecting Incongruent News Headlines using Mutual Attentive Semantic Matching

Measuring the congruence between two texts has several useful applications, such as detecting the prevalent deceptive and misleading news headlines on the web. Many works have proposed machine learning based solutions such as text similarity between the headline and body text to detect the incongruence. Text similarity based methods fail to perform well due to different inherent challenges such as relative length mismatch between the news headline and its body content and non-overlapping vocabulary. On the other hand, more recent works that use headline guided attention to learn a headline derived contextual representation of the news body also result in convoluting overall representation due to the news body's lengthiness. This paper proposes a method that uses inter-mutual attention-based semantic matching between the original and synthetically generated headlines, which utilizes the difference between all pairs of word embeddings of words involved. The paper also investigates two more variations of our method, which use concatenation and dot-products of word embeddings of the words of original and synthetic headlines. We observe that the proposed method outperforms prior arts significantly for two publicly available datasets.Comment: Accepted paper; IEEE 2020 International Conference on Machine Learning and Applications (ICMLA

Tackling Sequence to Sequence Mapping Problems with Neural Networks

In Natural Language Processing (NLP), it is important to detect the relationship between two sequences or to generate a sequence of tokens given another observed sequence. We call the type of problems on modelling sequence pairs as sequence to sequence (seq2seq) mapping problems. A lot of research has been devoted to finding ways of tackling these problems, with traditional approaches relying on a combination of hand-crafted features, alignment models, segmentation heuristics, and external linguistic resources. Although great progress has been made, these traditional approaches suffer from various drawbacks, such as complicated pipeline, laborious feature engineering, and the difficulty for domain adaptation. Recently, neural networks emerged as a promising solution to many problems in NLP, speech recognition, and computer vision. Neural models are powerful because they can be trained end to end, generalise well to unseen examples, and the same framework can be easily adapted to a new domain. The aim of this thesis is to advance the state-of-the-art in seq2seq mapping problems with neural networks. We explore solutions from three major aspects: investigating neural models for representing sequences, modelling interactions between sequences, and using unpaired data to boost the performance of neural models. For each aspect, we propose novel models and evaluate their efficacy on various tasks of seq2seq mapping.Comment: PhD thesi

Measuring Semantic Similarity: Representations and Methods

This dissertation investigates and proposes ways to quantify and measure semantic similarity between texts. The general approach is to rely on linguistic information at various levels, including lexical, lexico-semantic, and syntactic. The approach starts by mapping texts onto structured representations that include lexical, lexico-semantic, and syntactic information. The representation is then used as input to methods designed to measure the semantic similarity between texts based on the available linguistic information.While world knowledge is needed to properly assess semantic similarity of texts, in our approach world knowledge is not used, which is a weakness of it.We limit ourselves to answering the question of how successfully one can measure the semantic similarity of texts using just linguistic information.The lexical information in the original texts is retained by using the words in the corresponding representations of the texts. Syntactic information is encoded using dependency relations trees, which represent explicitly the syntactic relations between words. Word-level semantic information is relatively encoded through the use of semantic similarity measures like WordNet Similarity or explicitly encoded using vectorial representations such as Latent Semantic Analysis (LSA). Several methods are being studied to compare the representations, ranging from simple lexical overlap, to more complex methods such as comparing semantic representations in vector spaces as well as syntactic structures. Furthermore, a few powerful kernel models are proposed to use in combination with Support Vector Machine (SVM) classifiers for the case in which the semantic similarity problem is modeled as a classification task

Simple Convolutional Neural Networks with Linguistically-Annotated Input for Answer Selection in Question Answering

With the advent of deep learning methods, researchers have been increasingly preferring deep learning methods over decades-old feature-engineering-inspired work in Natural Language Processing (NLP). The research community has been moving away from otherwise dominant feature engineering approaches; rather, is gravitating towards more complicated neural architectures. Highly competitive tools like part-of-speech taggers that exhibit human-like accuracy are traded off for complex networks, with the hope that the neural network will learn the features needed. In fact, there have been efforts to do NLP "from scratch" with neural networks that altogether eschew featuring engineering based tools (Collobert et al, 2011). In our research, we modify the input that is fed to neural networks by annotating the input with linguistic information: POS tags, Named Entity Recognition output, linguistic relations, etc. With just the addition of these linguistic features on a simple Siamese convolutional neural network, we are able to achieve state-of-the-art results. We argue that this strikes a better balance between feature vs. network engineering

Adapting Automatic Summarization to New Sources of Information

English-language news articles are no longer necessarily the best source of information. The Web allows information to spread more quickly and travel farther: first-person accounts of breaking news events pop up on social media, and foreign-language news articles are accessible to, if not immediately understandable by, English-speaking users. This thesis focuses on developing automatic summarization techniques for these new sources of information. We focus on summarizing two specific new sources of information: personal narratives, first-person accounts of exciting or unusual events that are readily found in blog entries and other social media posts, and non-English documents, which must first be translated into English, often introducing translation errors that complicate the summarization process. Personal narratives are a very new area of interest in natural language processing research, and they present two key challenges for summarization. First, unlike many news articles, whose lead sentences serve as summaries of the most important ideas in the articles, personal narratives provide no such shortcuts for determining where important information occurs in within them; second, personal narratives are written informally and colloquially, and unlike news articles, they are rarely edited, so they require heavier editing and rewriting during the summarization process. Non-English documents, whether news or narrative, present yet another source of difficulty on top of any challenges inherent to their genre: they must be translated into English, potentially introducing translation errors and disfluencies that must be identified and corrected during summarization. The bulk of this thesis is dedicated to addressing the challenges of summarizing personal narratives found on the Web. We develop a two-stage summarization system for personal narrative that first extracts sentences containing important content and then rewrites those sentences into summary-appropriate forms. Our content extraction system is inspired by contextualist narrative theory, using changes in writing style throughout a narrative to detect sentences containing important information; it outperforms both graph-based and neural network approaches to sentence extraction for this genre. Our paraphrasing system rewrites the extracted sentences into shorter, standalone summary sentences, learning to mimic the paraphrasing choices of human summarizers more closely than can traditional lexicon- or translation-based paraphrasing approaches. We conclude with a chapter dedicated to summarizing non-English documents written in low-resource languages – documents that would otherwise be unreadable for English-speaking users. We develop a cross-lingual summarization system that performs even heavier editing and rewriting than does our personal narrative paraphrasing system; we create and train on large amounts of synthetic errorful translations of foreign-language documents. Our approach produces fluent English summaries from disdisfluent translations of non-English documents, and it generalizes across languages