United Arab Emirates

Creating classifiers of disinformation is time-consuming, expensive, and requires vast effort from experts spanning different fields. Even when these efforts succeed, their roll-out to publicly available applications stagnates. While these models struggle to find their consumer-accessible use, disinformation behavior online evolves at a pressing speed. The hoaxes get shared in various abbreviations on social networks, often in user-restricted areas, making external monitoring and intervention virtually impossible. To re-purpose existing NLP methods for the new paradigm of sharing misinformation, we propose leveraging information about given texts&#39; originating news sources to proxy the respective text&#39;s trustworthiness. We first present a methodology for determining the sources&#39; overall credibility. We demonstrate our pipeline construction in a specific language and introduce CNSC: a novel dataset for Czech articles&#39; news source and source credibility classification. We constitute initial benchmarks on multiple architectures. Lastly, we create in-the-wild wrapper applications of the trained models: a chatbot, a browser extension, and a standalone web application.

EMNLP 2022

Misinformation Detection in the Wild: News Source Classification as a Proxy for Non-article Texts

articles

disinformation

news

misinformation

detection

dataset

Creating classifiers of disinformation is time-consuming, expensive, and requires vast effort from experts spanning different fields. Even when these efforts succeed, their roll-out to publicly available applications stagnates. While these models struggle to find their consumer-accessible use, disinformation behavior online evolves at a pressing speed. The hoaxes get shared in various abbreviations on social networks, often in user-restricted areas, making external monitoring and intervention virtually impossible. To re-purpose existing NLP methods for the new paradigm of sharing misinformation, we propose leveraging information about given texts' originating news sources to proxy the respective text's trustworthiness. We first present a methodology for determining the sources' overall credibility. We demonstrate our pipeline construction in a specific language and introduce CNSC: a novel dataset for Czech articles' news source and source credibility classification. We constitute initial benchmarks on multiple architectures. Lastly, we create in-the-wild wrapper applications of the trained models: a chatbot, a browser extension, and a standalone web application.

workshop paper

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It is important to understand how people use words to persuade each other. This helps understand debate, and detect persuasive narratives in regard to e.g. misinformation. While computational modelling of some aspects of persuasion has received some attention, a way to unify and describe the overall phenomenon of when persuasion becomes undesired and problematic, is missing. In this paper, we attempt to address this by proposing a taxonomy of computational persuasion. Drawing upon existing research and resources, this paper shows how to re-frame and re-organise current work into a coherent framework targeting the misuse of rhetorical appeals. As a study to validate these re-framings, we then train and evaluate models of persuasion adapted to our taxonomy. Our results show an application of our taxonomy, and we are able to detecting misuse of rhetorical appeals, finding that these are more often used in misinformative contexts than in true ones.

Modelling Persuasion through Misuse of Rhetorical Appeals

With the identification of the inequality encoded in information acquisition among social classes, we propose to leverage a powerful concept that has never been studied as a linguistic construct, "fun", to deconstruct the inequality. Inspired by theories in sociology, we draw connection between social class and information cocoon, through the lens of fun, and hypothesize the measurement of "how fun one's dominating social cocoon is" to be an indicator of the social class of an individual. Following this, we propose an NLP framework to combat the issue by measuring how fun one's information cocoon is, and empower individuals to emancipate from their trapped cocoons. We position our work to be a domain-agnostic framework that can be deployed in a lot of downstream cases, and is one that aims to deconstruct, as opposed to reinforcing, the traditional social structure of beneficiaries.

Breaking through Inequality of Information Acquisition among Social Classes: A Modest Effort on Measuring "Fun"

We present a web application for creating games and exercises for teaching English as a foreign language with the help of NLP tools. The application contains different kinds of games such as crosswords, word searches, a memory game, and a multiplayer game based on the classic battleship pen and paper game. This application was built with the aim of supporting teachers in rural schools that are teaching English lessons, so they can easily create interactive and engaging activities for their students. We present the context and history of the project, the current state of the web application, and some ideas on how we will expand it in the future.

Using NLP to Support English Teaching in Rural Schools

Providing the right amount of explanation in an employment interview can help the interviewee effectively communicate their skills and experience to the interviewer and convince the she/he is the right candidate for the job. This paper examines natural language processing (NLP) approaches, including word-based tokenization, lexicon-based representations, and pre-trained embeddings with deep learning models, for detecting the degree of explanation in a job interview response. These are exemplified in a study of 24 military veterans who are the focal group of this study, since they can experience unique challenges in job interviews due to the unique verbal communication style that is prevalent in the military. Military veterans conducted mock interviews with industry recruiters and data from these interviews were transcribed and analyzed. Results indicate that  the feasibility of automated NLP methods for detecting the degree of explanation in an interview response. Features based on tokenizer analysis are the most effective in detecting under-explained responses (i.e., 0.29 F1-score), while lexicon-based methods depict the higher performance in detecting over-explanation (i.e., 0.51 F1-score). Findings from this work lay the foundation for the design of intelligent assistive technologies that can provide personalized learning pathways to job candidates, especially those belonging to sensitive or under-represented populations, and helping them succeed in employment job interviews, ultimately contributing to an inclusive workforce.

"Am I Answering My Job Interview Questions Right?'': A NLP Approach to Predict Degree of Explanation in Job Interview Responses

Patronizing and condescending language is characterized, among others, by its subtle nature. It thus seems reasonable to assume that detecting condescending language in text would be harder than detecting more explicitly harmful language, such as hate speech. However, the results of a SemEval-2022 Task devoted to this topic paint a different picture, with the top-performing systems achieving remarkably strong results. In this paper, we analyse the surprising effectiveness of standard text classification methods in more detail. In particular, we highlight the presence of two rather different types of condescending language in the dataset from the SemEval task. Some inputs are condescending because of the way they talk about a particular subject, i.e. condescending language in this case is a linguistic phenomenon, which can, in principle, be learned from training examples. However, other inputs are condescending because of the nature of what is said, rather than the way in which it is expressed, e.g. by emphasizing stereotypes about a given community. In such cases, our ability to detect condescending language, with current methods, largely depends on the presence of similar examples in the training data.

Identifying Condescending Language: A Tale of Two Distinct Phenomena?

A young child's vocabulary size is correlated with their level of personal wellbeing and future academic success. Yet, interventions aimed at increasing early vocabulary would ideally be tailored to each individual child's needs and interests, and such personalization would be impossible without technological support. Here, we explore if and how natural language processing can be used to create individualized bedtime stories around target words to be learned by preschoolers. Generating stories from scratch is challenging and often results in stories of low quality. Thus, we propose an alternative approach: completing phrase-level gaps within prewritten stories. On this task, we explore the performance of GPT-3 with and without finetuning as well as with and without providing a word which is semantically related to the target word. Manual evaluation of the generated stories shows that GPT-3 and GPT-3-based models perform well on the task. Using GPT-3 without finetuning and including a context word into the prompt is the best performing approach.

Generate Me a Bedtime Story: Leveraging Natural Language Processing for Early Vocabulary Enhancement

In this paper, we introduce a conversational agent (chatbot) for Hindi-speaking youth called BELA—Bot for English Language Acquisition. Developed for young underprivileged students at an Indian non-profit, the agent supports both Hindi and Hinglish (code-switched Hindi and English, written primarily with English orthography) utterances. BELA has two interaction modes: a question-answering mode for classic English language learning tasks like word meanings, translations, reading passage comprehensions, etc., and an open-domain dialogue system mode to allow users to practice their language skills.

We present a high-level overview of the design of BELA, including the implementation details and the preliminary results of our early prototype. We also report the challenges in creating an English-language learning chatbot for a largely Hindi-speaking population.

BELA: Bot for English Language Acquisition

The various potential of children can be limited by language delay or language impairments. However, there are many instances where parents are unaware of the child's condition and do not obtain appropriate treatment as a result.  Additionally, experts collecting children's utterance to establish norms of language tests and evaluating children's language development level takes a significant amount of time and work. To address these issues, dependable automated screening tools are required. In this paper, we used pretrained LM to assist experts in quickly and objectively screening the language development level of children. Here, evaluating the language development level is to ensure that the child has the appropriate language abilities for his or her age, which is the same as the child's age. To do this, we analyzed the utterances of children according to age. Based on these findings, we use the standard deviations of the pretrained LM's probability as a score for children to screen their language development level. The experiment results showed very strong correlations between our proposed method and the Korean language test REVT (REVT-R, REVT-E), with Pearson correlation coefficient of 0.9888 and 0.9892, respectively.

Applicability of Pretrained Language Models: Automatic Screening for Children's Language Development Level

Traditional sentiment analysis is a sentence level or document-level task. However, a sentence or paragraph may contain multiple target terms with different sentiments, making sentiment prediction more challenging. Although pre-trained language models like BERT have been successful, incorporating dynamic semantic changes into aspect-based sentiment models remains difficult, especially for domain-specific sentiment analysis. To this end, in this paper, we propose a Term-Based Sentiment Analysis (TBSA), a novel method designed to learn Environmental, Social, and Governance (ESG) contexts based on a sustainability taxonomy for ESG aspect-oriented sentiment analysis. Notably, we introduce a technique enhancing the ESG term's attention, inspired by the success of attention-based neural networks in machine translation (Bahdanau et al., 2015) and Computer Vision (Bello et al., 2019). It enables the proposed model to focus on a small region of the sentences at each step and to reweigh the crucial terms for a better understanding of the ESG aspect-aware sentiment. Beyond the novelty in the model design, we propose a new dataset of 125,000+ ESG analyst annotated data points for sustainability term based sentiment classification, which derives from historical sustainability corpus data and expertise acquired by development finance institutions. Our extensive experiments combining the new method and the new dataset demonstrate the effectiveness of the Sustainability TBSA model with an accuracy of 91.30\% (90\% F1-score). Both internal and external business applications of our model show an evident potential for a significant positive impact toward furthering sustainable development goals (SDGs).

Transformers-Based Approach for a Sustainability Term-Based Sentiment Analysis (STBSA)

Hateful memes are a growing menace on social media. While the image and its corresponding text in a meme are related, they do not necessarily convey the same meaning when viewed individually. Hence, detecting hateful memes requires careful consideration of both visual and textual information. Multimodal pre-training can be beneficial for this task because it effectively captures the relationship between the image and the text by representing them in a similar feature space. Furthermore, it is essential to model the interactions between the image and text features through intermediate fusion. Most existing methods either employ multimodal pre-training or intermediate fusion, but not both. In this work, we propose the Hate-CLIPper architecture, which explicitly models the cross-modal interactions between the image and text representations obtained using Contrastive Language-Image Pre-training (CLIP) encoders via a feature interaction matrix (FIM). A simple classifier based on the FIM representation is able to achieve state-of-the-art performance on the Hateful Memes Challenge (HMC) dataset with an AUROC of 85.8, which even surpasses the human performance of 82.65. Experiments on other meme datasets such as Propaganda Memes and TamilMemes also demonstrate the generalizability of the proposed approach. Finally, we analyze the interpretability of the FIM representation and show that cross-modal interactions can indeed facilitate the learning of meaningful concepts. The code for this work is available at https://github.com/gokulkarthik/hateclipper

Misinformation Detection in the Wild: News Source Classification as a Proxy for Non-article Texts

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Modelling Persuasion through Misuse of Rhetorical Appeals

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