Exploiting Group Structures to Infer Social Interactions From Videos

In this thesis, we consider the task of inferring the social interactions between humans by analyzing multi-modal data. Specifically, we attempt to solve some of the problems in interaction analysis, such as long-term deception detection, political deception detection, and impression prediction. In this work, we emphasize the importance of using knowledge about the group structure of the analyzed interactions. Previous works on the matter mostly neglected this aspect and analyzed a single subject at a time. Using the new Resistance dataset, collected by our collaborators, we approach the problem of long-term deception detection by designing a class of histogram-based features and a novel class of meta-features we callLiarRank. We develop a LiarOrNot model to identify spies in Resistance videos. We achieve AUCs of over 0.70 outperforming our baselines by 3% and human judges by 12%. For the problem of political deception, we first collect a dataset of videos and transcripts of 76 politicians from 18 countries making truthful and deceptive statements. We call it the Global Political Deception Dataset. We then show how to analyze the statements in a broader context by building a Video-Article-Topic graph. From this graph, we create a novel class of features called Deception Score that captures how controversial each topic is and how it affects the truthfulness of each statement. We show that our approach achieves 0.775 AUC outperforming competing baselines. Finally, we use the Resistance data to solve the problem of dyadic impression prediction. Our proposed Dyadic Impression Prediction System (DIPS) contains four major innovations: a novel class of features called emotion ranks, sign imbalance features derived from signed graphs theory, a novel method to align the facial expressions of subjects, and finally, we propose the concept of a multilayered stochastic network we call Temporal Delayed Network. Our DIPS architecture beats eight baselines from the literature, yielding statistically significant improvements of 19.9-30.8% in AUC

Detecting Check-Worthy Claims in Political Debates, Speeches, and Interviews Using Audio Data

A large portion of society united around the same vision and ideas carries enormous energy. That is precisely what political figures would like to accumulate for their cause. With this goal in mind, they can sometimes resort to distorting or hiding the truth, unintentionally or on purpose, which opens the door for misinformation and disinformation. Tools for automatic detection of check-worthy claims would be of great help to moderators of debates, journalists, and fact-checking organizations. While previous work on detecting check-worthy claims has focused on text, here we explore the utility of the audio signal as an additional information source. We create a new multimodal dataset (text and audio in English) containing 48 hours of speech. Our evaluation results show that the audio modality together with text yields improvements over text alone in the case of multiple speakers. Moreover, an audio-only model could outperform a text-only one for a single speaker.Comment: check-worthy claims, fake news, political debates, audi

Multimodal Automated Fact-Checking: A Survey

Misinformation is often conveyed in multiple modalities, e.g. a miscaptioned image. Multimodal misinformation is perceived as more credible by humans, and spreads faster than its text-only counterparts. While an increasing body of research investigates automated fact-checking (AFC), previous surveys mostly focus on text. In this survey, we conceptualise a framework for AFC including subtasks unique to multimodal misinformation. Furthermore, we discuss related terms used in different communities and map them to our framework. We focus on four modalities prevalent in real-world fact-checking: text, image, audio, and video. We survey benchmarks and models, and discuss limitations and promising directions for future researchComment: The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP): Finding

Multimodal Argument Mining: A Case Study in Political Debates

We propose a study on multimodal argument mining in the domain of political debates. We collate and extend existing corpora and provide an initial empirical study on multimodal architectures, with a special emphasis on input encoding methods. Our results provide interesting indications about future directions in this important domain

Detecting Deception, Partisan, and Social Biases

Tesis por compendio[ES] En la actualidad, el mundo político tiene tanto o más impacto en la sociedad que ésta en el mundo político. Los líderes o representantes de partidos políticos hacen uso de su poder en los medios de comunicación, para modificar posiciones ideológicas y llegar al pueblo con el objetivo de ganar popularidad en las elecciones gubernamentales.A través de un lenguaje engañoso, los textos políticos pueden contener sesgos partidistas y sociales que minan la percepción de la realidad. Como resultado, los seguidores de una ideología, o miembros de una categoría social, se sienten amenazados por otros grupos sociales o ideológicos, o los perciben como competencia, derivándose así una polarización política con agresiones físicas y verbales. La comunidad científica del Procesamiento del Lenguaje Natural (NLP, según sus siglas en inglés) contribuye cada día a detectar discursos de odio, insultos, mensajes ofensivos, e información falsa entre otras tareas computacionales que colindan con ciencias sociales. Sin embargo, para abordar tales tareas, es necesario hacer frente a diversos problemas entre los que se encuentran la dificultad de tener textos etiquetados, las limitaciones de no trabajar con un equipo interdisciplinario, y los desafíos que entraña la necesidad de soluciones interpretables por el ser humano. Esta tesis se enfoca en la detección de sesgos partidistas y sesgos sociales, tomando como casos de estudio el hiperpartidismo y los estereotipos sobre inmigrantes. Para ello, se propone un modelo basado en una técnica de enmascaramiento de textos capaz de detectar lenguaje engañoso incluso en temas controversiales, siendo capaz de capturar patrones del contenido y el estilo de escritura. Además, abordamos el problema usando modelos basados en BERT, conocidos por su efectividad al capturar patrones sintácticos y semánticos sobre las mismas representaciones de textos. Ambos enfoques, la técnica de enmascaramiento y los modelos basados en BERT, se comparan en términos de desempeño y explicabilidad en la detección de hiperpartidismo en noticias políticas y estereotipos sobre inmigrantes. Para la identificación de estos últimos, se propone una nueva taxonomía con fundamentos teóricos en sicología social, y con la que se etiquetan textos extraídos de intervenciones partidistas llevadas a cabo en el Parlamento español. Los resultados muestran que los enfoques propuestos contribuyen al estudio del hiperpartidismo, así como a identif i car cuándo los ciudadanos y políticos enmarcan a los inmigrantes en una imagen de víctima, recurso económico, o amenaza. Finalmente, en esta investigación interdisciplinaria se demuestra que los estereotipos sobre inmigrantes son usados como estrategia retórica en contextos políticos.[CA] Avui, el món polític té tant o més impacte en la societat que la societat en el món polític. Els líders polítics, o representants dels partits polítics, fan servir el seu poder als mitjans de comunicació per modif i car posicions ideològiques i arribar al poble per tal de guanyar popularitat a les eleccions governamentals. Mitjançant un llenguatge enganyós, els textos polítics poden contenir biaixos partidistes i socials que soscaven la percepció de la realitat. Com a resultat, augmenta la polarització política nociva perquè els seguidors d'una ideologia, o els membres d'una categoria social, veuen els altres grups com una amenaça o competència, que acaba en agressions verbals i físiques amb resultats desafortunats. La comunitat de Processament del llenguatge natural (PNL) té cada dia noves aportacions amb enfocaments que ajuden a detectar discursos d'odi, insults, missatges ofensius i informació falsa, entre altres tasques computacionals relacionades amb les ciències socials. No obstant això, molts obstacles impedeixen eradicar aquests problemes, com ara la dif i cultat de tenir textos anotats, les limitacions dels enfocaments no interdisciplinaris i el repte afegit per la necessitat de solucions interpretables. Aquesta tesi se centra en la detecció de biaixos partidistes i socials, prenent com a cas pràctic l'hiperpartidisme i els estereotips sobre els immigrants. Proposem un model basat en una tècnica d'emmascarament que permet detectar llenguatge enganyós en temes polèmics i no polèmics, capturant pa-trons relacionats amb l'estil i el contingut. A més, abordem el problema avaluant models basats en BERT, coneguts per ser efectius per capturar patrons semàntics i sintàctics en la mateixa representació. Comparem aquests dos enfocaments (la tècnica d'emmascarament i els models basats en BERT) en termes de rendiment i les seves solucions explicables en la detecció de l'hiperpartidisme en les notícies polítiques i els estereotips d'immigrants. Per tal d'identificar els estereotips dels immigrants, proposem una nova tax-onomia recolzada per la teoria de la psicologia social i anotem un conjunt de dades de les intervencions partidistes al Parlament espanyol. Els resultats mostren que els nostres models poden ajudar a estudiar l'hiperpartidisme i identif i car diferents marcs en què els ciutadans i els polítics perceben els immigrants com a víctimes, recursos econòmics o amenaces. Finalment, aquesta investigació interdisciplinària demostra que els estereotips dels immigrants s'utilitzen com a estratègia retòrica en contextos polítics.[EN] Today, the political world has as much or more impact on society than society has on the political world. Political leaders, or representatives of political parties, use their power in the media to modify ideological positions and reach the people in order to gain popularity in government elections. Through deceptive language, political texts may contain partisan and social biases that undermine the perception of reality. As a result, harmful political polarization increases because the followers of an ideology, or members of a social category, see other groups as a threat or competition, ending in verbal and physical aggression with unfortunate outcomes. The Natural Language Processing (NLP) community has new contri-butions every day with approaches that help detect hate speech, insults, of f ensive messages, and false information, among other computational tasks related to social sciences. However, many obstacles prevent eradicating these problems, such as the dif f i culty of having annotated texts, the limitations of non-interdisciplinary approaches, and the challenge added by the necessity of interpretable solutions. This thesis focuses on the detection of partisan and social biases, tak-ing hyperpartisanship and stereotypes about immigrants as case studies. We propose a model based on a masking technique that can detect deceptive language in controversial and non-controversial topics, capturing patterns related to style and content. Moreover, we address the problem by evalu-ating BERT-based models, known to be ef f ective at capturing semantic and syntactic patterns in the same representation. We compare these two approaches (the masking technique and the BERT-based models) in terms of their performance and the explainability of their decisions in the detection of hyperpartisanship in political news and immigrant stereotypes. In order to identify immigrant stereotypes, we propose a new taxonomy supported by social psychology theory and annotate a dataset from partisan interventions in the Spanish parliament. Results show that our models can help study hyperpartisanship and identify dif f erent frames in which citizens and politicians perceive immigrants as victims, economic resources, or threat. Finally, this interdisciplinary research proves that immigrant stereotypes are used as a rhetorical strategy in political contexts.This PhD thesis was funded by the MISMIS-FAKEnHATE research project (PGC2018-096212-B-C31) of the Spanish Ministry of Science and Innovation.Sánchez Junquera, JJ. (2022). Detecting Deception, Partisan, and Social Biases [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/185784Compendi

When a few words are not enough: improving text classification through contextual information

Traditional text classification approaches may be ineffective when applied to texts with insufficient or limited number of words due to brevity of text and sparsity of feature space. The lack of contextual information can make texts ambiguous; hence, text classification approaches relying solely on words may not properly capture the critical features of a real-world problem. One of the popular approaches to overcoming this problem is to enrich texts with additional domain-specific features. Thus, this thesis shows how it can be done in two realworld problems in which text information alone is insufficient for classification. While one problem is depression detection based on the automatic analysis of clinical interviews, another problem is detecting fake online news. Depression profoundly affects how people behave, perceive, and interact. Language reveals our ideas, moods, feelings, beliefs, behaviours and personalities. However, because of inherent variations in the speech system, no single cue is sufficiently discriminative as a sign of depression on its own. This means that language alone may not be adequate for understanding a person’s mental characteristics and states. Therefore, adding contextual information can properly represent the critical features of texts. Speech includes both linguistic content (what people say) and acoustic aspects (how words are said), which provide important clues about the speaker’s emotional, physiological and mental characteristics. Therefore, we study the possibility of effectively detecting depression using unobtrusive and inexpensive technologies based on the automatic analysis of language (what you say) and speech (how you say it). For fake news detection, people seem to use their cognitive abilities to hide information, which induces behavioural change, thereby changing their writing style and word choices. Therefore, the spread of false claims has polluted the web. However, the claims are relatively short and include limited content. Thus, capturing only text features of the claims will not provide sufficient information to detect deceptive claims. Evidence articles can help support the factual claim by representing the central content of the claim more authentically. Therefore, we propose an automated credibility assessment approach based on linguistic analysis of the claim and its evidence articles

University Authors, 2020

https://digitalcommons.montclair.edu/univ-authors/1009/thumbnail.jp

Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

The eighth edition of the Italian Conference on Computational Linguistics (CLiC-it 2021) was held at Università degli Studi di Milano-Bicocca from 26th to 28th January 2022. After the edition of 2020, which was held in fully virtual mode due to the health emergency related to Covid-19, CLiC-it 2021 represented the first moment for the Italian research community of Computational Linguistics to meet in person after more than one year of full/partial lockdown