181 research outputs found
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 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
A Corpus Driven Computational Intelligence Framework for Deception Detection in Financial Text
Financial fraud rampages onwards seemingly uncontained. The annual cost of fraud in the UK is estimated to be as high as £193bn a year [1] . From a data science perspective and hitherto less explored this thesis demonstrates how the use of linguistic features to drive data mining algorithms can aid in unravelling fraud. To this end, the spotlight is turned on Financial Statement Fraud (FSF), known to be the costliest type of fraud [2]. A new corpus of 6.3 million words is composed of102 annual reports/10-K (narrative sections) from firms formally indicted for FSF juxtaposed with 306 non-fraud firms of similar size and industrial grouping. Differently from other similar studies, this thesis uniquely takes a wide angled view and extracts a range of features of different categories from the corpus. These linguistic correlates of deception are uncovered using a variety of techniques and tools. Corpus linguistics methodology is applied to extract keywords and to examine linguistic structure. N-grams are extracted to draw out collocations. Readability measurement in financial text is advanced through the extraction of new indices that probe the text at a deeper level. Cognitive and perceptual processes are also picked out. Tone, intention and liquidity are gauged using customised word lists. Linguistic ratios are derived from grammatical constructs and word categories. An attempt is also made to determine ‘what’ was said as opposed to ‘how’. Further a new module is developed to condense synonyms into concepts. Lastly frequency counts from keywords unearthed from a previous content analysis study on financial narrative are also used. These features are then used to drive machine learning based classification and clustering algorithms to determine if they aid in discriminating a fraud from a non-fraud firm. The results derived from the battery of models built typically exceed classification accuracy of 70%. The above process is amalgamated into a framework. The process outlined, driven by empirical data demonstrates in a practical way how linguistic analysis could aid in fraud detection and also constitutes a unique contribution made to deception detection studies
USING DEEP LEARNING AND LINGUISTIC ANALYSIS TO PREDICT FAKE NEWS WITHIN TEXT
The spread of information about current events is a way for everybody in the world to learn and understand what is happening in the world. In essence, the news is an important and powerful tool that could be used by various groups of people to spread awareness and facts for the good of mankind. However, as information becomes easily and readily available for public access, the rise of deceptive news becomes an increasing concern. The reason is due to the fact that it will cause people to be misled and thus could affect the livelihood of themselves or others. The term that is coined for spreading false information is known as fake news. It is of the utmost importance to mitigate this issue, thus the proposition is to perform a study on technological techniques that are being used to prevent the spread of dishonest and propagandized information. Since there are an abundance of websites and articles that internet users could read, the use of automated technology was the only logical option when dealing with fake news. The techniques that were used in this study were based around linguistic analysis and deep learning. The end objective was to create a classifier that was able to judge an article based on the amount of fake news within it. Experiments were performed on these classifiers, which tried to prove that applied linguistic analysis was important in improving the accuracy of the classifiers. The results from this study displayed evidence that applied linguistic analysis did not have a sufficient impact, whereas deep learning and dataset improvements did have an impact
A Multilingual Spam Reviews Detection Based on Pre-Trained Word Embedding and Weighted Swarm Support Vector Machines
Online reviews are important information that customers seek when deciding to buy products or
services. Also, organizations benefit from these reviews as essential feedback for their products or services.
Such information required reliability, especially during the Covid-19 pandemic which showed a massive
increase in online reviews due to quarantine and sitting at home. Not only the number of reviews was boosted
but also the context and preferences during the pandemic. Therefore, spam reviewers reflect on these changes
and improve their deception technique. Spam reviews usually consist of misleading, fake, or fraudulent
reviews that tend to deceive customers for the purpose of making money or causing harm to other competitors.
Hence, this work presents a Weighted Support Vector Machine (WSVM) and Harris Hawks Optimization
(HHO) for spam review detection. The HHO works as an algorithm for optimizing hyperparameters and
feature weighting. Three different language corpora have been used as datasets, namely English, Spanish, and
Arabic in order to solve the multilingual problem in spam reviews. Moreover, pre-trained word embedding
(BERT) has been applied alongside three-word representation methods (NGram-3, TFIDF, and One-hot
encoding). Four experiments have been conducted, each focused on solving and demonstrating different
aspects. In all experiments, the proposed approach showed excellent results compared with other state-ofthe-
art algorithms. In other words, the WSVM-HHO achieved an accuracy of 88.163%, 71.913%, 89.565%,
and 84.270%, for English, Spanish, Arabic, and Multilingual datasets, respectively. Further, a deep analysis
has been conducted to investigate the context of reviews before and after the COVID-19 situation. In addition,
it has been generated to create a new dataset with statistical features and merge its previous textual features
for improving detection performance.Projects TED2021-129938B-I0,PID2020-113462RB-I00, PDC2022-133900-I00PID2020-115570GB-C22, granted by Ministerio Español de Ciencia e InnovaciónMCIN/AEI/10.13039/501100011033MCIN/AEI/10.13039/501100011033MCIN/AEINext GenerationEU/PRT
Deep Multi Temporal Scale Networks for Human Motion Analysis
The movement of human beings appears to respond to a complex motor system that contains signals at different hierarchical levels.
For example, an action such as ``grasping a glass on a table'' represents a high-level action, but to perform this task, the body needs several motor inputs that include the activation of different joints of the body (shoulder, arm, hand, fingers, etc.).
Each of these different joints/muscles have a different size, responsiveness, and precision with a complex non-linearly stratified temporal dimension where every muscle has its temporal scale.
Parts such as the fingers responds much faster to brain input than more voluminous body parts such as the shoulder.
The cooperation we have when we perform an action produces smooth, effective, and expressive movement in a complex multiple temporal scale cognitive task.
Following this layered structure, the human body can be described as a kinematic tree, consisting of joints connected.
Although it is nowadays well known that human movement and its perception are characterised by multiple temporal scales, very few works in the literature are focused on studying this particular property.
In this thesis, we will focus on the analysis of human movement using data-driven techniques.
In particular, we will focus on the non-verbal aspects of human movement, with an emphasis on full-body movements.
The data-driven methods can interpret the information in the data by searching for rules, associations or patterns that can represent the relationships between input (e.g. the human action acquired with sensors) and output (e.g. the type of action performed).
Furthermore, these models may represent a new research frontier as they can analyse large masses of data and focus on aspects that even an expert user might miss.
The literature on data-driven models proposes two families of methods that can process time series and human movement.
The first family, called shallow models, extract features from the time series that can help the learning algorithm find associations in the data.
These features are identified and designed by domain experts who can identify the best ones for the problem faced.
On the other hand, the second family avoids this phase of extraction by the human expert since the models themselves can identify the best set of features to optimise the learning of the model.
In this thesis, we will provide a method that can apply the multi-temporal scales property of the human motion domain to deep learning models, the only data-driven models that can be extended to handle this property.
We will ask ourselves two questions: what happens if we apply knowledge about how human movements are performed to deep learning models? Can this knowledge improve current automatic recognition standards?
In order to prove the validity of our study, we collected data and tested our hypothesis in specially designed experiments.
Results support both the proposal and the need for the use of deep multi-scale models as a tool to better understand human movement and its multiple time-scale nature
Human-Computer Interaction: Security Aspects
Along with the rapid development of intelligent information age, users are having a growing interaction with smart devices.
Such smart devices are interconnected together in the Internet of Things (IoT).
The sensors of IoT devices collect information about users' behaviors from the interaction between users and devices.
Since users interact with IoT smart devices for the daily communication and social network activities, such interaction generates a huge amount of network traffic.
Hence, users' behaviors are playing an important role in the security of IoT smart devices, and the security aspects of Human-Computer Interaction are becoming significant.
In this dissertation, we provide a threefold contribution:
(1) we review security challenges of HCI-based authentication, and design a tool to detect deceitful users via keystroke dynamics; (2) we present the impact of users' behaviors on network traffic, and propose a framework to manage such network traffic; (3) we illustrate a proposal for energy-constrained IoT smart devices to be resilient against energy attack and efficient in network communication.
More in detail, in the first part of this thesis, we investigate how users' behaviors impact on the way they interact with a device.
Then we review the work related to security challenges of HCI-based authentication on smartphones, and Brain-Computer Interfaces (BCI).
Moreover, we design a tool to assess the truthfulness of the information that users input using a computer keyboard.
This tool is based on keystroke dynamics and it relies on machine learning technique to achieve this goal.
To the best of our knowledge, this is the first work that associates the typing users' behaviors with the production of deceptive personal information.
We reached an overall accuracy of 76% in the classification of a single answer as truthful or deceptive.
In the second part of this thesis, we review the analysis of network traffic, especially related to the interaction between mobile devices and users.
Since the interaction generates a huge amount of network traffic, we propose an innovative framework, GolfEngine, to manage and control the impact of users behavior on the network relying on Software Defined Networking (SDN) techniques.
GolfEngine provides users a tool to build their security applications and offers Graphical User Interface (GUI) for managing and monitoring the network.
In particular, GolfEngine provides the function of checking policy conflicts when users design security applications and the mechanism to check data storage redundancy.
GolfEngine not only prevents the malicious inputting policies but also it enforces the security about network management of network traffic.
The results of our simulation underline that GolfEngine provides an efficient, secure, and robust performance for managing network traffic via SDN.
In the third and last part of this dissertation, we analyze the security aspects of battery-equipped IoT devices from the energy consumption perspective.
Although most of the energy consumption of IoT devices is due to user interaction, there is still a significant amount of energy consumed by point-to-point communication and IoT network management.
In this scenario, an adversary may hijack an IoT device and conduct a Denial of Service attack (DoS) that aims to run out batteries of other devices.
Therefore, we propose EnergIoT, a novel method based on energetic policies that prevent such attacks and, at the same time, optimizes the communication between users and IoT devices, and extends the lifetime of the network.
EnergIoT relies on a hierarchical clustering approach, based on different duty cycle ratios, to maximize network lifetime of energy-constrained smart devices.
The results show that EnergIoT enhances the security and improves the network lifetime by 32%, compared to the earlier used approach, without sacrificing the network performance (i.e., end-to-end delay)
Towards Evaluating Veracity of Textual Statements on the Web
The quality of digital information on the web has been disquieting due to the absence of careful checking. Consequently, a large volume of false textual information is being produced and disseminated with misstatements of facts. The potential negative influence on the public, especially in time-sensitive emergencies, is a growing concern. This concern has motivated this thesis to deal with the problem of veracity evaluation. In this thesis, we set out to develop machine learning models for the veracity evaluation of textual claims based on stance and user engagements. Such evaluation is achieved from three aspects: news stance detection engaged user replies in social media and the engagement dynamics. First of all, we study stance detection in the context of online news articles where a claim is predicted to be true if it is supported by the evidential articles. We propose to manifest a hierarchical structure among stance classes: the high-level aims at identifying relatedness, while the low-level aims at classifying, those identified as related, into the other three classes, i.e., agree, disagree, and discuss. This model disentangles the semantic difference of related/unrelated and the other three stances and helps address the class imbalance problem. Beyond news articles, user replies on social media platforms also contain stances and can infer claim veracity. Claims and user replies in social media are usually short and can be ambiguous; to deal with semantic ambiguity, we design a deep latent variable model with a latent distribution to allow multimodal semantic distribution. Also, marginalizing the latent distribution enables the model to be more robust in relatively smalls-sized datasets. Thirdly, we extend the above content-based models by tracking the dynamics of user engagement in misinformation propagation. To capture these dynamics, we formulate user engagements as a dynamic graph and extract its temporal evolution patterns and geometric features based on an attention-modified Temporal Point Process. This allows to forecast the cumulative number of engaged users and can be useful in assessing the threat level of an individual piece of misinformation. The ability to evaluate veracity and forecast the scale growth of engagement networks serves to practically assist the minimization of online false information’s negative impacts
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