Brazil

In view of the emergence of mobility restrictions and social isolation imposed by the coronavirus or COVID-19 pandemic, digital media, especially social networks, become a breeding ground for fake news, political attacks and large-scale misinformation. The impacts of this `infodemic&#39; can take even greater proportions when using sponsored content on social networks, such as Facebook ads. Using the Facebook ad library we collected more than 236k facebook ads from 75 different countries. Choosing ads from Brazil as the focus of research, we found ads with political attacks, requests for donations, doctors prescribing vitamin D as a weapon to fight coronavirus, among other contents with evidence of misinformation.

WebMedia 2020

Analyzing the Use of Covid-19 Ads on Facebook

facebook ads

political advertisement

covid-19 ads

Promoted by the Brazilian Computer Society (SBC), the Brazilian Symposium on Multimedia and Web (WebMedia) is the main event of the theme in Brazil and an excellent opportunity for scientific and technical exchange among students, researchers and professionals in the areas of Multimedia, Hypermedia and Web. This year we will be celebrating to 26th edition of WebMedia, which will be held online from november 30th to December 4th .

Webmedia 2020’s programming includes:

– Technical sessions for the presentation of scientific papers
– National and international keynotes
– Panel with companies
– Workshops
– Short Courses
– Open community meetings

The topics of interest for submitting papers are:

– Web and Social Networks
– Multimedia
– Digital TV, Ubiquitous and Mobile Computing

Besides the main track, WebMedia 2020 will have in parallel the Tools and Applications Workshop, works Tools and Applications Workshop, the Workshop on Undergraduate Research Work and the Workshop The Future of Interactive.

The WebMedia 2020 organization is under the responsibility of the Programa de Pós-graduação Doutorado em Ciência da Computação – Associação UFMA/UFPI (DCCMAPI), do Programa de Pós-graduação em Ciência da Computação/ CCET (PPGCC), do Laboratory of Advanced Web Systems/UFMA (LAWS) e do Laboratório de Sistemas Multimídia/UFMA (TELEMÍDIA).).

#PartiuWebMedia2020 #WebMedia2020

Promoted by the Brazilian Computer Society (SBC), the Brazilian Symposium on Multimedia and Web (WebMedia) is the main event of the theme in Brazil and an excellent opportunity for scientific and technical exchange among students, researchers and professionals in the areas of Multimedia, Hypermedia and Web.

In view of the emergence of mobility restrictions and social isolation imposed by the coronavirus or COVID-19 pandemic, digital media, especially social networks, become a breeding ground for fake news, political attacks and large-scale misinformation. The impacts of this `infodemic' can take even greater proportions when using sponsored content on social networks, such as Facebook ads. Using the Facebook ad library we collected more than 236k facebook ads from 75 different countries. Choosing ads from Brazil as the focus of research, we found ads with political attacks, requests for donations, doctors prescribing vitamin D as a weapon to fight coronavirus, among other contents with evidence of misinformation.

technical paper

In the last decades, the dissemination of News through digital media has increased the information accessibility previously offered by traditional channels. Despite their benefits, digital media have exacerbated an old problem: the spread of Fake News, (i.e., false News intentionally published). Faced with this scenario, the linguistic approaches to automatic Fake News detection use information that can be directly extracted from the News' text. Several methods based on these approaches use grammatical classification and sentiment analysis over News writing in Portuguese. However, as far as it was possible to observe in the related literature, these methods are limited to the identification of polarity of sentiment (i.e., positive, neutral or negative) existing in the text. Although polarity classification be an effective method for a wide range of natural language processing applications, it does not address language nuances (e.g., emotions such as anger, sadness, etc.) that can provide evidence that a text contains false information. Hence, this study proposes an extended method that, in addition to the grammatical classification and polarity based sentiment analysis, also uses the analysis of emotions to detect Fake News written in Portuguese. The extended method showed promising results in experimental data, obtaining accuracy greater than 92\%. In average, the proposed method overcame polarity and gramatical classification based methods in 1.4 percentage points.

A Linguistic-Based Method that Combines Polarity, Emotion and Grammatical Characteristics to Detect Fake News in Portuguese

As smartphones increased in popularity, mobile devices became a crucial technological element in Information Systems. Beyond the end-user platform, smartphones have several sensors to capture and characterise the user's context. Current Context-Aware Mobile (CAM) applications use this data to improve user experience by filtering data, services, and, even, adapting the system behavior. The design and development of the capture, inference, and action parts of CAM applications can be challenging for mobile developers, due to the diversity of mobile hardware ecosystem and sensor APIs' complexity. This paper presents EasyContext, a visual tool for designing and generating contextual rules for mobile devices, using the Google Awareness API as its contextual information provider. Mobile developers specify what to capture and when to react to context-changes in the visual tool and these contextual rules are exported to Android projects. We evaluated our approach with eight developers, which were selected to participate in a quasi-experiment. The experiment involves developing two mobile applications using both EasyContext and Google Awareness API. Preliminary results show that it is possible to build applications with fewer lines of code compared to the Google Awareness API. According to the developers, the proposed approach has better readability and makes it easier to design contextual rules.

A Generative Approach for Android Sensor-based Applications

Research indicates that people can receive more useful product recommendations if the filtering process considers their personality. In this paper, we propose a hybrid strategy for Recommender Systems (using matrix factorization and personality-based neighborhood) to recommend the best products calculated for a particular customer (user). The proposed user profile used in the definition of the neighborhood involves these three personality models: Big Five (or OCEAN, or Five-Factor Model), Needs, and Values. We experimented with data from more than 10,000 Amazon customers. We inferred their personality characteristics from the analysis of reviews via IBM Watson Personality Insights. The results indicated that the proposed strategy's performance was better than that of the state-of-the-art algorithms analyzed. Besides, there was no statistical difference between using only the Big Five model or using it together with the Needs and Values models.

Collaborative Filtering Strategy for Product Recommendation Using Personality Characteristics of Customers

Sistemas de recomendação são ferramentas que auxiliam usuários em seu processo de decisão e quando aplicados ao turismo, esses sistemas visam facilitar o planejamento de viagens e escolha de destinos. Apesar de todos os esforços para se construir tais sistemas e otimizar o tempo dos usuários, questões como o atendimento das expectativas dos turistas e a motivação para viajar ainda são aspectos negligenciados. Neste trabalho, será apresentado uma abordagem que considera a perspectiva de visitas, considerando práticas de turismo alternativas que possibilitam experiências diversificadas, proporcionando aos turistas uma experiência diferenciada, permitindo que os mesmos vivenciem as cidades sob a perspectiva dos moradores locais.

Uma abordagem para sistemas de recomendação orientado a perspectivas de experiências turísticas


The widespread of fake news is increasingly worrying society anddemanding approaches for mitigation. Although many approacheshave been proposed to fake news detection, there is still a lack ofworks that deeply investigate their structure. Our study has beenmotivated by two findings discussed in existing works: the firstis the fact that fake news usually mix real and fake informationto mislead readers; the second is that subjective language is a re-source commonly exploited by fake news producers. Therefore,to better understand how fake news is structured, we perform ananalysis on the way the subjective language is exploited in differ-ent situations inside the fake news documents. For this, we builta dataset by manually identifying fake parts of news articles, andwe also propose tags to categorize the fragments in documents.The proposed tags categorize the fake news fragments according totheir kind of falsehood in document. To reveal subjectivity nuanceswithin the fragments, we use the Word Movers Distance and a setof subjectivity lexicons in the Portuguese language. Our resultsindicate that the fragmentation of news allows the identificationof subjectivity markers that cannot be identified when consideringthe entire documents.

Analysis of the Subjectivity Level in Fake News Fragments

We present a multimodal dataset of songs with structured information to be applied in various machine learning tasks. The data comes from a platform focused on lyrics information, but contain several other annotations provided by their users. Our dataset, called 4MuLA (Multitask, Multimodal, and Multilingual Music Lyrics and Audio features dataset), contains features extracted from 96,458 songs distributed by 15,310 artists in 76 genres. Most of the songs in 4MuLA are protected by copyrights, so we can not provide the original audio. For each track, we make available various acoustic features, extracted tags, and their lyrics in English, Portuguese, or Spanish. With these features, researchers can use our dataset for, at least, lyrics-, audio- or multimodal-based genre classification, music and artist similarity, and popularity regression. Moreover, we can perform cross- or multi-lingual text analysis on lyrics, such as discourse analysis or measuring the differences between emotion transmitted by audio and lyrics.

4MuLA: A Multitask, Multimodal, and Multilingual Dataset of Music Lyrics and Audio Features

Deaf people are a considerable part of the world population and communicate naturally using sign languages. However, although many countries adopt their sign language as an official language, there are linguistics barriers to accessing fundamental rights, especially access to health services, even more critical situation in the midst of the COVID-19 crisis. This situation has been the focus of some government policies that oblige essential service providers to provide sign language interpreters to assist Deaf people. However, this type of solution has high operating costs, mainly to serve the entire Deaf community in all environments. These setbacks motivate the investigation of methodologies and automated tools to
support this type of problem. In this paper, we address this problem by proposing two-stream model for the recognition of the Brazilian Sign Language (Libras) in the health context. The proposed solution does not use any additional capture sensor or hardware, being entirely base on images or sequences of images (videos). The
results show that the solution is able to recognize the Libras signs in the test dataset reasonably well, achieved an average accuracy of approximately 96,12% considering a scenario where the interpreter used in the test set was not used in the training set, which shows that there are good evidence that it can assist in the communication
process with Deaf people. Besides, an additional contribution of this paper is the introduction of a new dataset in the Brazilian sign language (Libras) containing 5000 videos of 50 signs in the health context, which may assist the development and research of other solutions.

A Two-Stream Model Based on 3D Convolutional Neural Networks for the Recognition of Brazilian Sign Language in the Health Context

The Internet of Things (IoT) is a Ubiquitous Computing materialization that is gaining prominence. Security has become potentialized as a necessary feature for a viable IoT. Promoting security over this dynamic and heterogeneous environment with pre-defined and static security mechanisms is an unfeasible task. Therefore, solutions for adaptive security are required. This paper has as main objective to present the conception of a proposal for adaptive security through progressive context-based enrichment considering the challenges of IoT scenarios, offering as a differential the contextual information acquisition in the opportune moment and contemplating heterogeneous sources. To evaluate this proposal was developed one case study to validate the progressive contextualization of security events. Finally, the strengths and limitations of our approach are discussed, followed by future works.

AS-ProCBE: An Architectural Model for Adaptive Security through Progressive Context-Based Enrichment

The growth and diversity of data sources have been a challenge for data interoperability. Although there are several initiatives to face this challenge, there are still difficulties especially when data are at different levels of abstraction. In this sense, this article proposes INTEGRA, an RDF-based approach, which takes advantage of the graph flexibility. It allows the integration of heterogeneous data sources through interconnections between schemas, between instances, and between instance-schema. Additionally, we present a case study in the Software Development Management scenario, which shows INTEGRA feasibility and its benefits.

An RDF based approach for integrating data at different levels of abstraction

Lexical Substitution has a noticeable evolution in the literature,
mainly in the data sources used for the generation of substitutes
that feed the process. Of course, dictionaries and thesauri are widely
used for grouping synonyms in their structure, but the polysemy of
words prevents a direct exchange of terms without analyzing the
context. Vector space models, such as embeddings, are used to rep-
resent substitutes and also contexts. However, the representation of
words considering only contextual factors, in many cases, may in-
cur the approximation of terms that are not exactly synonyms. The
characteristics mentioned above suggest that the simultaneous use
of dictionaries and embeddings is a promising alternative for the
process. Thus, we present a method using information contained in
merged dictionaries, in addition to their linguistic relations struc-
tured in taxonomies. The method measures the preservation of the
meaning of the sentence with the potential synonym by observing
its frequency of application in small contexts. In addition, we also
consider a complete context to generate input from vector opera-
tions highlighting the best synonyms in a previously selected set.
The results show the efficiency of the method, surpassing many
methods consolidated in the literature in the prediction of the best
substitute for words contained in instances of a known benchmark.

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