Human-in-the-Loop Hate Speech Classification in a Multilingual Context

The shift of public debate to the digital sphere has been accompanied by a rise in online hate speech. While many promising approaches for hate speech classification have been pro- posed, studies often focus only on a single language, usually English, and do not address three key concerns: post-deployment perfor- mance, classifier maintenance and infrastruc- tural limitations. In this paper, we introduce a new human-in-the-loop BERT-based hate speech classification pipeline and trace its de- velopment from initial data collection and an- notation all the way to post-deployment. Our classifier, trained using data from our original corpus of over 422k examples, is specifically developed for the inherently multilingual set- ting of Switzerland and outperforms with its F1 score of 80.5 the currently best-performing BERT-based multilingual classifier by 5.8 F1 points in German and 3.6 F1 points in French. Our systematic evaluations over a 12-month period further highlight the vital importance of continuous, human-in-the-loop classifier main- tenance to ensure robust hate speech classifica- tion post-deployment

Joint Modelling of Emotion and Abusive Language Detection

The rise of online communication platforms has been accompanied by some undesirable effects, such as the proliferation of aggressive and abusive behaviour online. Aiming to tackle this problem, the natural language processing (NLP) community has experimented with a range of techniques for abuse detection. While achieving substantial success, these methods have so far only focused on modelling the linguistic properties of the comments and the online communities of users, disregarding the emotional state of the users and how this might affect their language. The latter is, however, inextricably linked to abusive behaviour. In this paper, we present the first joint model of emotion and abusive language detection, experimenting in a multi-task learning framework that allows one task to inform the other. Our results demonstrate that incorporating affective features leads to significant improvements in abuse detection performance across datasets.Comment: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 202

Mapping (Dis-)Information Flow about the MH17 Plane Crash

Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to circulation of disinformation on social media is the MH17 plane crash. Studies analysing the spread of information about this event on Twitter have focused on small, manually annotated datasets, or used proxys for data annotation. In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though we find that a neural classifier improves over a hashtag based baseline, labeling pro-Russian and pro-Ukrainian content with high precision remains a challenging problem. We provide an error analysis underlining the difficulty of the task and identify factors that might help improve classification in future work. Finally, we show how the classifier can facilitate the annotation task for human annotators

Automatic detection of hate speech in text: an overview of the topic and dataset annotation with hierarchical classes

Nowadays people are using more and more social networks to communicate their opinions, share information and experiences. In social networks people have the feeling of being deindividualized and can incur more frequently in aggressive communication. In this context, it is important that government and social networks platforms have tools to detect hate speech because it is harmful to its targets. In our work we investigate the problem of detecting hate speech online. Our first goal is to make a complete overview on the topic. However, describing the state of the art in the area of hate speech is not simple, because this topic is regarded by different areas, such as text mining, social sciences, and law. Our literature review is focused on the perspective of computer science and engineering and it is distinct from other works we found. We adopted an exhaustive and methodical method. We called it Systematic Literature Review. As a result, we concluded that the majority of the studies tackles this problem as a machine learning classification task and the studies use either general text mining features (e.g n-grams, word2vec), or hate speech specific features (e.g othering discourse). In the majority of the studies new datasets are collected, but those remain private, which makes more difficult to compare the results across the different studies. We concluded also that this field is still in an early stage, with several open research opportunities. As we found no research on the topic in Portuguese, the second goal of this work was to annotate a dataset for this language. Regarding the dataset annotation, we built a classification using a hierarchical structure. This is an innovative way of approaching the problem of hate speech automatic classification. Its main advantage is that it allows to better consider nuances in the hate speech concepts. We collect a dataset with 5,668 messages, from 1156 distinct users, annotated not only for hate speech, but also for more 83 subtypes of hate.Finally, we also try to prove that the hierarchical structure of classes used also allows to improve the performance of the classification models, since it is better suited for consider the different subtypes of hate speech and the intersections between those classes

Collective moderation of hate, toxicity, and extremity in online discussions

How can citizens moderate hate, toxicity, and extremism in online discourse? We analyze a large corpus of more than 130,000 discussions on German Twitter over the turbulent four years marked by the migrant crisis and political upheavals. With a help of human annotators, language models, machine learning classifiers, and longitudinal statistical analyses, we discern the dynamics of different dimensions of discourse. We find that expressing simple opinions, not necessarily supported by facts but also without insults, relates to the least hate, toxicity, and extremity of speech and speakers in subsequent discussions. Sarcasm also helps in achieving those outcomes, in particular in the presence of organized extreme groups. More constructive comments such as providing facts or exposing contradictions can backfire and attract more extremity. Mentioning either outgroups or ingroups is typically related to a deterioration of discourse in the long run. A pronounced emotional tone, either negative such as anger or fear, or positive such as enthusiasm and pride, also leads to worse outcomes. Going beyond one-shot analyses on smaller samples of discourse, our findings have implications for the successful management of online commons through collective civic moderation

Sentiment Analysis for Social Media

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection

How can humans leverage machine learning? From Medical Data Wrangling to Learning to Defer to Multiple Experts

Mención Internacional en el título de doctorThe irruption of the smartphone into everyone’s life and the ease with which we digitise or record any data supposed an explosion of quantities of data. Smartphones, equipped with advanced cameras and sensors, have empowered individuals to capture moments and contribute to the growing pool of data. This data-rich landscape holds great promise for research, decision-making, and personalized applications. By carefully analyzing and interpreting this wealth of information, valuable insights, patterns, and trends can be uncovered. However, big data is worthless in a vacuum. Its potential value is unlocked only when leveraged to drive decision-making. In recent times we have been participants of the outburst of artificial intelligence: the development of computer systems and algorithms capable of perceiving, reasoning, learning, and problem-solving, emulating certain aspects of human cognitive abilities. Nevertheless, our focus tends to be limited, merely skimming the surface of the problem, while the reality is that the application of machine learning models to data introduces is usually fraught. More specifically, there are two crucial pitfalls frequently neglected in the field of machine learning: the quality of the data and the erroneous assumption that machine learning models operate autonomously. These two issues have established the foundation for the motivation driving this thesis, which strives to offer solutions to two major associated challenges: 1) dealing with irregular observations and 2) learning when and who should we trust. The first challenge originates from our observation that the majority of machine learning research primarily concentrates on handling regular observations, neglecting a crucial technological obstacle encountered in practical big-data scenarios: the aggregation and curation of heterogeneous streams of information. Before applying machine learning algorithms, it is crucial to establish robust techniques for handling big data, as this specific aspect presents a notable bottleneck in the creation of robust algorithms. Data wrangling, which encompasses the extraction, integration, and cleaning processes necessary for data analysis, plays a crucial role in this regard. Therefore, the first objective of this thesis is to tackle the frequently disregarded challenge of addressing irregularities within the context of medical data. We will focus on three specific aspects. Firstly, we will tackle the issue of missing data by developing a framework that facilitates the imputation of missing data points using relevant information derived from alternative data sources or past observations. Secondly, we will move beyond the assumption of homogeneous observations, where only one statistical data type (such as Gaussian) is considered, and instead, work with heterogeneous observations. This means that different data sources can be represented by various statistical likelihoods, such as Gaussian, Bernoulli, categorical, etc. Lastly, considering the temporal enrichment of todays collected data and our focus on medical data, we will develop a novel algorithm capable of capturing and propagating correlations among different data streams over time. All these three problems are addressed in our first contribution which involves the development of a novel method based on Deep Generative Models (DGM) using Variational Autoencoders (VAE). The proposed model, the Sequential Heterogeneous Incomplete VAE (Shi- VAE), enables the aggregation of multiple heterogeneous data streams in a modular manner, taking into consideration the presence of potential missing data. To demonstrate the feasibility of our approach, we present proof-of-concept results obtained from a real database generated through continuous passive monitoring of psychiatric patients. Our second challenge relates to the misbelief that machine learning algorithms can perform independently. However, this notion that AI systems can solely account for automated decisionmaking, especially in critical domains such as healthcare, is far from reality. Our focus now shifts towards a specific scenario where the algorithm has the ability to make predictions independently or alternatively defer the responsibility to a human expert. The purpose of including the human is not to obtain jsut better performance, but also more reliable and trustworthy predictions we can rely on. In reality, however, important decisions are not made by one person but are usually committed by an ensemble of human experts. With this in mind, two important questions arise: 1) When should the human or the machine bear responsibility and 2) among the experts, who should we trust? To answer the first question, we will employ a recent theory known as Learning to defer (L2D). In L2D we are not only interested in abstaining from prediction but also in understanding the humans confidence for making such prediction. thus deferring only when the human is more likely to be correct. The second question about who to defer among a pool of experts has not been yet answered in the L2D literature, and this is what our contributions aim to provide. First, we extend the two yet proposed consistent surrogate losses in the L2D literature to the multiple-expert setting. Second, we study the frameworks ability to estimate the probability that a given expert correctly predicts and assess whether the two surrogate losses are confidence calibrated. Finally, we propose a conformal inference technique that chooses a subset of experts to query when the system defers. Ensembling experts based on confidence levels is vital to optimize human-machine collaboration. In conclusion, this doctoral thesis has investigated two cases where humans can leverage the power of machine learning: first, as a tool to assist in data wrangling and data understanding problems and second, as a collaborative tool where decision-making can be automated by the machine or delegated to human experts, fostering more transparent and trustworthy solutions.La irrupción de los smartphones en la vida de todos y la facilidad con la que digitalizamos o registramos cualquier situación ha supuesto una explosión en la cantidad de datos. Los teléfonos, equipados con cámaras y sensores avanzados, han contribuido a que las personas puedann capturar más momentos, favoreciendo así el creciente conjunto de datos. Este panorama repleto de datos aporta un gran potencial de cara a la investigación, la toma de decisiones y las aplicaciones personalizadas. Mediante el análisis minucioso y una cuidada interpretación de esta abundante información, podemos descubrir valiosos patrones, tendencias y conclusiones Sin embargo, este gran volumen de datos no tiene valor por si solo. Su potencial se desbloquea solo cuando se aprovecha para impulsar la toma de decisiones. En tiempos recientes, hemos sido testigos del auge de la inteligencia artificial: el desarrollo de sistemas informáticos y algoritmos capaces de percibir, razonar, aprender y resolver problemas, emulando ciertos aspectos de las capacidades cognitivas humanas. No obstante, solemos centrarnos solo en la superficie del problema mientras que la realidad es que la aplicación de modelos de aprendizaje automático a los datos presenta desafíos significativos. Concretamente, se suelen pasar por alto dos problemas cruciales en el campo del aprendizaje automático: la calidad de los datos y la suposición errónea de que los modelos de aprendizaje automático pueden funcionar de manera autónoma. Estos dos problemas han sido el fundamento de la motivación que impulsa esta tesis, que se esfuerza en ofrecer soluciones a dos desafíos importantes asociados: 1) lidiar con datos irregulares y 2) aprender cuándo y en quién debemos confiar. El primer desafío surge de nuestra observación de que la mayoría de las investigaciones en aprendizaje automático se centran principalmente en manejar datos regulares, descuidando un obstáculo tecnológico crucial que se encuentra en escenarios prácticos con gran cantidad de datos: la agregación y el curado de secuencias heterogéneas. Antes de aplicar algoritmos de aprendizaje automático, es crucial establecer técnicas robustas para manejar estos datos, ya que est problemática representa un cuello de botella claro en la creación de algoritmos robustos. El procesamiento de datos (en concreto, nos centraremos en el término inglés data wrangling), que abarca los procesos de extracción, integración y limpieza necesarios para el análisis de datos, desempeña un papel crucial en este sentido. Por lo tanto, el primer objetivo de esta tesis es abordar el desafío normalmente paso por alto de tratar datos irregulare. Específicamente, bajo el contexto de datos médicos. Nos centraremos en tres aspectos principales. En primer lugar, abordaremos el problema de los datos perdidos mediante el desarrollo de un marco que facilite la imputación de estos datos perdidos utilizando información relevante obtenida de fuentes de datos de diferente naturalaeza u observaciones pasadas. En segundo lugar, iremos más allá de la suposición de lidiar con observaciones homogéneas, donde solo se considera un tipo de dato estadístico (como Gaussianos) y, en su lugar, trabajaremos con observaciones heterogéneas. Esto significa que diferentes fuentes de datos pueden estar representadas por diversas distribuciones de probabilidad, como Gaussianas, Bernoulli, categóricas, etc. Por último, teniendo en cuenta el enriquecimiento temporal de los datos hoy en día y nuestro enfoque directo sobre los datos médicos, propondremos un algoritmo innovador capaz de capturar y propagar la correlación entre diferentes flujos de datos a lo largo del tiempo. Todos estos tres problemas se abordan en nuestra primera contribución, que implica el desarrollo de un método basado en Modelos Generativos Profundos (Deep Genarative Model en inglés) utilizando Autoencoders Variacionales (Variational Autoencoders en ingés). El modelo propuesto, Sequential Heterogeneous Incomplete VAE (Shi-VAE), permite la agregación de múltiples flujos de datos heterogéneos de manera modular, teniendo en cuenta la posible presencia de datos perdidos. Para demostrar la viabilidad de nuestro enfoque, presentamos resultados de prueba de concepto obtenidos de una base de datos real generada a través del monitoreo continuo pasivo de pacientes psiquiátricos. Nuestro segundo desafío está relacionado con la creencia errónea de que los algoritmos de aprendizaje automático pueden funcionar de manera independiente. Sin embargo, esta idea de que los sistemas de inteligencia artificial pueden ser los únicos responsables en la toma de decisione, especialmente en dominios críticos como la atención médica, está lejos de la realidad. Ahora, nuestro enfoque se centra en un escenario específico donde el algoritmo tiene la capacidad de realizar predicciones de manera independiente o, alternativamente, delegar la responsabilidad en un experto humano. La inclusión del ser humano no solo tiene como objetivo obtener un mejor rendimiento, sino también obtener predicciones más transparentes y seguras en las que podamos confiar. En la realidad, sin embargo, las decisiones importantes no las toma una sola persona, sino que generalmente son el resultado de la colaboración de un conjunto de expertos. Con esto en mente, surgen dos preguntas importantes: 1) ¿Cuándo debe asumir la responsabilidad el ser humano o cuándo la máquina? y 2) de entre los expertos, ¿en quién debemos confiar? Para responder a la primera pregunta, emplearemos una nueva teoría llamada Learning to defer (L2D). En L2D, no solo estamos interesados en abstenernos de hacer predicciones, sino también en comprender cómo de seguro estará el experto para hacer dichas predicciones, diferiendo solo cuando el humano sea más probable en predecir correcatmente. La segunda pregunta sobre a quién deferir entre un conjunto de expertos aún no ha sido respondida en la literatura de L2D, y esto es precisamente lo que nuestras contribuciones pretenden proporcionar. En primer lugar, extendemos las dos primeras surrogate losses consistentes propuestas hasta ahora en la literatura de L2D al contexto de múltiples expertos. En segundo lugar, estudiamos la capacidad de estos modelos para estimar la probabilidad de que un experto dado haga predicciones correctas y evaluamos si estas surrogate losses están calibradas en términos de confianza. Finalmente, proponemos una técnica de conformal inference que elige un subconjunto de expertos para consultar cuando el sistema decide diferir. Esta combinación de expertos basada en los respectivos niveles de confianza es fundamental para optimizar la colaboración entre humanos y máquinas En conclusión, esta tesis doctoral ha investigado dos casos en los que los humanos pueden aprovechar el poder del aprendizaje automático: primero, como herramienta para ayudar en problemas de procesamiento y comprensión de datos y, segundo, como herramienta colaborativa en la que la toma de decisiones puede ser automatizada para ser realizada por la máquina o delegada a expertos humanos, fomentando soluciones más transparentes y seguras.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Joaquín Míguez Arenas.- Secretario: Juan José Murillo Fuentes.- Vocal: Mélanie Natividad Fernández Pradie

Detection and Prevention of Cyberbullying on Social Media

The Internet and social media have undoubtedly improved our abilities to keep in touch with friends and loved ones. Additionally, it has opened up new avenues for journalism, activism, commerce and entertainment. The unbridled ubiquity of social media is, however, not without negative consequences and one such effect is the increased prevalence of cyberbullying and online abuse. While cyberbullying was previously restricted to electronic mail, online forums and text messages, social media has propelled it across the breadth of the Internet, establishing it as one of the main dangers associated with online interactions. Recent advances in deep learning algorithms have progressed the state of the art in natural language processing considerably, and it is now possible to develop Machine Learning (ML) models with an in-depth understanding of written language and utilise them to detect cyberbullying and online abuse. Despite these advances, there is a conspicuous lack of real-world applications for cyberbullying detection and prevention. Scalability; responsiveness; obsolescence; and acceptability are challenges that researchers must overcome to develop robust cyberbullying detection and prevention systems. This research addressed these challenges by developing a novel mobile-based application system for the detection and prevention of cyberbullying and online abuse. The application mitigates obsolescence by using different ML models in a “plug and play” manner, thus providing a mean to incorporate future classifiers. It uses ground truth provided by the enduser to create a personalised ML model for each user. A new large-scale cyberbullying dataset of over 62K tweets annotated using a taxonomy of different cyberbullying types was created to facilitate the training of the ML models. Additionally, the design incorporated facilities to initiate appropriate actions on behalf of the user when cyberbullying events are detected. To improve the app’s acceptability to the target audience, user-centred design methods were used to discover stakeholders’ requirements and collaboratively design the mobile app with young people. Overall, the research showed that (a) the cyberbullying dataset sufficiently captures different forms of online abuse to allow the detection of cyberbullying and online abuse; (b) the developed cyberbullying prevention application is highly scalable and responsive and can cope with the demands of modern social media platforms (b) the use of user-centred and participatory design approaches improved the app’s acceptability amongst the target audience

Automatic movie analysis and summarisation

Automatic movie analysis is the task of employing Machine Learning methods to the field of screenplays, movie scripts, and motion pictures to facilitate or enable various tasks throughout the entirety of a movie’s life-cycle. From helping with making informed decisions about a new movie script with respect to aspects such as its originality, similarity to other movies, or even commercial viability, all the way to offering consumers new and interesting ways of viewing the final movie, many stages in the life-cycle of a movie stand to benefit from Machine Learning techniques that promise to reduce human effort, time, or both. Within this field of automatic movie analysis, this thesis addresses the task of summarising the content of screenplays, enabling users at any stage to gain a broad understanding of a movie from greatly reduced data. The contributions of this thesis are four-fold: (i)We introduce ScriptBase, a new large-scale data set of original movie scripts, annotated with additional meta-information such as genre and plot tags, cast information, and log- and tag-lines. To our knowledge, Script- Base is the largest data set of its kind, containing scripts and information for almost 1,000 Hollywood movies. (ii) We present a dynamic summarisation model for the screenplay domain, which allows for extraction of highly informative and important scenes from movie scripts. The extracted summaries allow for the content of the original script to stay largely intact and provide the user with its important parts, while greatly reducing the script-reading time. (iii) We extend our summarisation model to capture additional modalities beyond the screenplay text. The model is rendered multi-modal by introducing visual information obtained from the actual movie and by extracting scenes from the movie, allowing users to generate visual summaries of motion pictures. (iv) We devise a novel end-to-end neural network model for generating natural language screenplay overviews. This model enables the user to generate short descriptive and informative texts that capture certain aspects of a movie script, such as its genres, approximate content, or style, allowing them to gain a fast, high-level understanding of the screenplay. Multiple automatic and human evaluations were carried out to assess the performance of our models, demonstrating that they are well-suited for the tasks set out in this thesis, outperforming strong baselines. Furthermore, the ScriptBase data set has started to gain traction, and is currently used by a number of other researchers in the field to tackle various tasks relating to screenplays and their analysis