A Domain-Independent Holistic Approach to Deception Detection (2021)

Abstract: The deception in the text can be of different forms in different domains, including fake news, rumor tweets, and spam emails. Irrespective of the domain, the main intent of the deceptive text is to deceit the reader. Although domain-specific deception detection exists, domain-independent deception detection can provide a holistic picture, which can be crucial to understand how deception occurs in the text. In this paper, we detect deception in a domain-independent setting using deep learning architectures. Our method outperforms the State-of-the-Art performance of most benchmark datasets with an overall accuracy of 93.42% and F1-Score of 93.22%. The domain-independent training allows us to capture subtler nuances of deceptive writing style. Furthermore, we analyze how much in-domain data may be helpful to accurately detect deception, especially for the cases where data may not be readily available to train. Our results and analysis indicate that there may be a universal pattern of deception lying in-between the text independent of the domain, which can create a novel area of research and open up new avenues in the field of deception detection.

## introduction in the current era of the flood of information, deception has become an undeniable event, causing a financial or political catastrophe and even the loss of human lives. often, we do not have the necessary resources to validate a tweet or a catchy social media forwarded news link. our idea is to capture the writing style hidden "between-the-lines", intended to deceive the reader. the deception can be in the form of any textual stream and on any topic. so, our objective is to find a holistic model that can leverage thousands of textual resources and find a learning architecture to decode the deception. adopting the definition of deception from burgoon and buller, we define deceptive text as any textual content that aims to misconstrue an affair in a deliberate way causing the reader at a disadvantage either directly or indirectly @xcite . deceptive text can be of various forms. for example, in the news and public media domain, the deceptive text is known as fake news. in social media, a deceptive text can happen in the form of a rumor. spam or a phishing email is treated as deceptive content in the personal mail or messaging domain. each domain's deceptive text has distinct ways to deceit the reader. while fake news can spread falsified propaganda, spam or phishing email can be used for merely monetary gain. therefore, the ways of formulating a deceptive text can have significant variations. notwithstanding, all deceptive texts share a common goal of tricking the reader and thus, a general deception pattern should exist in these texts. unravelling the pattern can play a pivotal role to provide a holistic view of deception, which in-turn can bolster the deception-detection. however, gröndahl and asokan indicated that existing works fail to generalize the deception across different domains @xcite . in this work, we hypothesize that-(h1) a deep learning architecture trained on a generalized deceptive-writing setting (both in-domain and out-of-domain data) can better understand the underlying general pattern of deception than using the in-domain data only. on the rise of a fairly new event, we may not have data available to detect deceptive text floating around social and mass media, especially the labeled data for supervised training. such occasions pose a unique challenge to stop the spread of misinformation. a holistic deception detection system can come in handy in such situations. for example, although the pandemic was as ancient as human civilization, in the age of massive data availability, covid-19 becomes a new event, and misinformation caused by this event can be hard to battle. therefore, we hypothesize that (h2) a generalized dataset can be helpful to detect deception in a new event, even when little or no in-domain data is available. to test our two hypotheses, we train and fine-tune a bert model, sbert model, and character-level-cnn model @xcite . we further investigate the intermediate-layer learning mechanism using t-sne visualization and attention-weight analysis. researchers worked meticulously to model deception on different domains, but a unified approach has not been successful. hernández-castañeda et al. suggested a cross-domain approach for a generalized deception-detection model @xcite . although they claimed to build a domain-independent system, their choice of the datasets, namely derev, opspam, and opinion (a dataset where the participants were told to lie about their opinion about selected topics), are of the type opinion-only. moreover, the opinion dataset can hardly be treated as deceptive. that is because when people are told to lie about an opinion, the lie may not have the potential of deceiving someone which contradicts our definition of deception. on the contrary, our choice of datasets have at least three variations of categories -fake news, twitter rumor, and spam. thus, we summarize the main contributions of this paper as-• our work is the first to propose a domainindependent holistic approach to detecting deception leveraging available public datasets. ## literature review in this research, we aim to detect deceptive content intended to mislead people rather than entertain them. therefore, the deceptive contents can be viewed as deceptive news, disinformation, cherry-picking, and click-bait @xcite . there have been several approaches for manual fact-checking, both in the form of expertbased (pol, accessed february 2, 2021; gos, accessed february 2, 2021; fac, accessed february 2, 2021) and crowd-sourced (cre, accessed february 2, 2021). however, given the enormous influx of information, such manual approaches are time-consuming and often biased. therefore, automated fact-checking came in handy. based on how users spread the falsified information, researchers adopted following approaches to detect deceptive content: news-cascade, which is a tree-like structure to analyse the propagation in social-media @xcite , and propagation graph @xcite . however, such approaches are also constrained by the availability of propagation detection resources. moreover, false information cannot be detected before it spreads out. additionally, some research tries to detect false news based on the source credibility @xcite . nevertheless, the stream of new sources now and then makes the task challenging. therefore an ai-based method aiming to detect deception based on the textual content can be handy. because of the fewer dependencies and availability of content, many researchers worked in that direction. zhou et al. divided the task into two steps: (i) how well the deceptive news content is captured, and (ii) how well the classification model performs to detect deception @xcite . approaches, such as the bag of words (bow) model, pos tagging, rhetorical relationships, were used as features to detect deceptive news @xcite @xcite . nevertheless, we are more interested in the semantics, as the task of deception may lie in between the text. word level context embeddings, including continuous bag of words (cbow) and skip-gram models were used to represent text for detecting fake news @xcite . along with such representations, several machine learning algorithms are used for classification purposes. additionally, with the rise of deep learning, convolutional neural network (cnn) and many variants of recurrent neural network (rnn) are used as well @xcite . recently the bidirectional encoder representations from transformers (bert) model and its variants have gained enormous popularity due to its pretraining capability @xcite . müller et al. proposed covid-twitter-bert (ct-bert), which is pretrained on covid-19 related twitter messages @xcite . such task-specific bert model outperforms the generic bert models in the significant margin in many covid-related classification tasks, including aaai2021 covid-19 shared challenge of covid-19 fake news detection @xcite . a similar fine-tuning approach is used by liu et al., where they proposed a two-stage approach for short fake news detection. unlike the original bert model, they utilized all hidden states to apply the attention mechanism to calculate weights for text representation. their approach produced a 34% accuracy in the liar dataset @xcite . kaliyar et al. proposed fakebert, which uses a cnn model after the bert embedding layer @xcite . although the current deception detection methods work well, the methods are highly dependent on the training of the specific domain. on the contrary, we intend to eliminate dataset-specific training and train our model for the generic deception detection task. ## dataset we curate ten datasets for our analysis. we broadly categorize them as-i) spam, ii) fake news, and iii) rumour. the details of the datasets are described below. ## results and discussion to test the hypotheses, we divide our experiments in two parts: i) general holistic deception detection and ii) new-event deception detection. ## conclusion and future work this research presents the holistic deception detection technique where we intend to find a domainindependent system to detect deception. our general holistic approach outperforms some of the benchmark datasets for deception detection, where we observe the superior performance of the bert model. additionally, we analyze the strength of our holistic approach in case of a new event like covid-19. we show that an out-of-domain general training set with a small fraction of the indomain training set can help achieve satisfactory performance. based on our work, there can be several directions for further research -• the bert and sbert model work for 512 tokens only. for deception within the long text, models like longformer, docbert can be used @xcite .