Publicaciones

AUTHORS

Evangelos Milios, Fernanda Weiss, Marcelo Mendoza

ABSTRACT

The automatic detection of rumors in social networks has gained considerable attention from researchers and practitioners during the last decade, due to the consequences of the spread of disinformation in public opinion. Most of the existing methods make use of features extracted from conversational threads, user profiles, and structural information of the network. These features are difficult to capture in practice and are often only partially available during the spread of rumors. In this paper, we study an unexplored approach in rumor detection: time series classification (TSC). By modeling the problem using time series, we avoid using lexical or structural characteristics of the network. Instead, we use information that is simpler to capture, such as the volume of tweets and the number of followers and followees of the users involved in a story. In this way, the characterization of the story is not related to specific users, but to variables aggregated at the event level.We introduce a TSC-based model for detecting rumors based on hypergraph partitioning, aligning time series prototypes with rumor classes. Our approach uses a Siamese network to train a rumor detection model in a supervised way, minimizing the distance between the time series of the training examples and the prototypes of their class. Results on benchmark data show that our approach surpasses other TSC-based methods in detecting rumors. Also, we compare our methods performance with methods that make use of lexical and structural characteristics. Our experiments show that our method has advantages in time-sensitive contexts, outperforming the state of the art in early detection scenarios with incomplete information.

AUTHORS

Marcelo Mendoza, Naim Bro

ABSTRACT

Based on a geocoded registry of more than four million residents of Santiago, Chile, we build two surname-based networks that reveal the city’s population structure. The first network is formed from paternal and maternal surname pairs. The second network is formed from the isonymic distances between the city’s neighborhoods. These networks uncover the city’s main ethnic groups and their spatial distribution. We match the networks to a socioeconomic index, and find that surnames of high socioeconomic status tend to cluster, be more diverse, and occupy a well-defined quarter of the city. The results are suggestive of a high degree of urban segregation in Santiago.

AUTHORS

Gabriel Molina, Ignacio Loayza, Marcelo Mendoza, Mauricio Araya, Mauricio Solar, Víctor Castañeda, Camilo Núñez

ABSTRACT

Medical images are an essential input for the timely diagnosis of pathologies. Despite its wide use in the area, searching for images that can reveal valuable information to support decision-making is difficult and expensive. However, the possibilities that open when making large repositories of images available for search by content are unsuspected. We designed a content-based image retrieval system for medical imaging, which reduces the gap between access to information and the availability of useful repositories to meet these needs. The system operates on the principle of query-by-example, in which users provide medical images, and the system displays a set of related images. Unlike metadata match-driven searches, our system drives content-based search. This allows the system to conduct searches on repositories of medical images that do not necessarily have complete and curated metadata. We explore our system’s feasibility in computational tomography (CT) slices for SARS-CoV-2 infection (COVID-19), showing that our proposal obtains promising results, advantageously comparing it with other search methods.

AUTHORS

Marcelo Mendoza, Carlos Aspillaga, Álvaro Soto

ABSTRACT

The field of natural language understanding has experienced exponential progress in the last few years, with impressive results in several tasks. This success has motivated researchers to study the underlying knowledge encoded by these models. Despite this, attempts to understand their semantic capabilities have not been successful, often leading to non-conclusive, or contradictory conclusions among different works. Via a probing classifier, we extract the underlying knowledge graph of nine of the most influential language models of the last years, including word embeddings, text generators, and context encoders. This probe is based on concept relatedness, grounded on WordNet. Our results reveal that all the models encode this knowledge, but suffer from several inaccuracies. Furthermore, we show that the different architectures and training strategies lead to different model biases. We conduct a systematic evaluation to discover specific factors that explain why some concepts are challenging. We hope our insights will motivate the development of models that capture concepts more precisely.

AUTHORS

Evangelos Milios, Ignacio Tampe, Marcelo Mendoza

ABSTRACT

Summarization has usually relied on gold standard summaries to train extractive or abstractive models. Social media brings a hurdle to summarization techniques since it requires addressing a multi-document multi-author approach. We address this challenging task by introducing a novel method that generates abstractive summaries of online news discussions. Our method extends a BERT-based architecture, including an attention encoding that fed comments' likes during the training stage. To train our model, we define a task which consists of reconstructing high impact comments based on popularity (likes). Accordingly, our model learns to summarize online discussions based on their most relevant comments. Our novel approach provides a summary that represents the most relevant aspects of a news item that users comment on, incorporating the social context as a source of information to summarize texts in online social networks. Our model is evaluated using ROUGE scores between the generated summary and each comment on the thread. Our model, including the social attention encoding, significantly outperforms both extractive and abstractive summarization methods based on such evaluation.

AUTHORS

Marcelo Mendoza, Sebastián Ruíz, Eliana Providel

ABSTRACT

Social networks are used every day to report daily events, although the information published in them many times correspond to fake news. Detecting these fake news has become a research topic that can be approached using deep learning. However, most of the current research on the topic is available only for the English language. When working on fake news detection in other languages, such as Spanish, one of the barriers is the low quantity of labeled datasets available in Spanish. Hence, we explore if it is convenient to translate an English dataset to Spanish using Statistical Machine Translation. We use the translated dataset to evaluate the accuracy of several deep learning architectures and compare the results from the translated dataset and the original dataset in fake news classification. Our results suggest that the approach is feasible, although it requires high-quality translation techniques, such as those found in the translation’s neural-based models.

AUTHORS

Víctor Castañeda, Marcelo Mendoza, Gabriel Molina, Gonzalo Pereira, Humberto Farías, Mauricio Araya, Mauricio Solar, Steffen Härtel, Camilo G Sotomayor

ABSTRACT

Medical imaging is essential nowadays throughout medical education, research, and care. Accordingly, international efforts have been made to set large-scale image repositories for these purposes. Yet, to date, browsing of large-scale medical image repositories has been troublesome, time-consuming, and generally limited by text search engines. A paradigm shift, by means of a query-by-example search engine, would alleviate these constraints and beneficially impact several practical demands throughout the medical field. The current project aims to address this gap in medical imaging consumption by developing a content-based image retrieval (CBIR) system, which combines two image processing architectures based on deep learning. Furthermore, a first-of-its-kind intelligent visual browser was designed that interactively displays a set of imaging examinations with similar visual content on a similarity map, making it possible to search for and efficiently navigate through a large-scale medical imaging repository, even if it has been set with incomplete and curated metadata. Users may, likewise, provide text keywords, in which case the system performs a content- and metadata-based search. The system was fashioned with an anonymizer service and designed to be fully interoperable according to international standards, to stimulate its integration within electronic healthcare systems and its adoption for medical education, research and care. Professionals of the healthcare sector, by means of a self-administered questionnaire, underscored that this CBIR system and intelligent interactive visual browser would be highly useful for these purposes. Further studies are warranted to complete a comprehensive assessment of the performance of the system through case description and protocolized evaluations by medical imaging specialists.

AUTHORS

Marcelo Mendoza

ABSTRACT

Los bots tienen un nefasto efecto en la diseminación de información engañosa o tendenciosa en redes sociales [1]. Su objetivo es amplificar la alcanzabilidad de campañas, transformando artificialmente mensajes en tendencias. Para ello, las cuentas que dan soporte a campañas se hacen seguir por cuentas manejadas por algoritmos. Muchas de las cuentas que siguen a personajes de alta connotación pública son bots, las cuales entregan soporte a sus mensajes con likes y retweets. Cuando estos mensajes muestran un inusitado nivel de reacciones, se transforman en tendencias, lo cual aumenta aún más su visibilidad. Al transformarse en tendencias, su influencia en la red crece, produciendo un fenómeno de bola de nieve.

AUTHORS

Marcelo Mendoza, Carlos Valle, Pablo Ormeño

ABSTRACT

Ad hoc information retrieval (ad hoc IR) is a challenging task consisting of ranking text documents for bag-of-words (BOW) queries. Classic approaches based on query and document text vectors use term-weighting functions to rank the documents. Some of these methods’ limitations consist of their inability to work with polysemic concepts. In addition, these methods introduce fake orthogonalities between semantically related words. To address these limitations, model-based IR approaches based on topics have been explored. Specifically, topic models based on Latent Dirichlet Allocation (LDA) allow building representations of text documents in the latent space of topics, the better modeling of polysemy and avoiding the generation of orthogonal representations between related terms. We extend LDA-based IR strategies using different ensemble strategies. Model selection obeys the ensemble learning paradigm, for which we test two successful approaches widely used in supervised learning. We study Boosting and Bagging techniques for topic models, using each model as a weak IR expert. Then, we merge the ranking lists obtained from each model using a simple but effective top-k list fusion approach. We show that our proposal strengthens the results in precision and recall, outperforming classic IR models and strong baselines based on topic models.

AUTHORS

Evangelos Milios, Ignacio Tampe, Marcelo Mendoza

ABSTRACT

Summarization has usually relied on gold standard summaries to train extractive or abstractive models. Social media brings a hurdle to summarization techniques since it requires addressing a multi-document multi-author approach. We address this challenging task by introducing a novel method that generates abstractive summaries of online news discussions. Our method extends a BERT-based architecture, including an attention encoding that fed comments’ likes during the training stage. To train our model, we define a task which consists of reconstructing high impact comments based on popularity (likes). Accordingly, our model learns to summarize online discussions based on their most relevant comments. Our novel approach provides a summary that represents the most relevant aspects of a news item that users comment on, incorporating the social context as a source of information to summarize texts in online social networks. Our model is evaluated using ROUGE scores between the generated summary and each comment on the thread. Our model, including the social attention encoding, significantly outperforms both extractive and abstractive summarization methods based on such evaluation.