Brazil

WhatsApp created a new channel for smartphone users to consume and share news. The easiness to create groups of people that partake similar interests and share content has made WhatsApp prone to abuse by misinformation campaigns. Although fact-checking is very effective for detecting misinformation, it cannot keep up with the sheer volume of information that is now generated online. In this context, we investigate the potential of automatic approaches based on supervised machine learning as a support tool to help fact-checkers identify misinformation shared through images on WhatsApp. Our results show that the predictive performance of the investigated approaches has a useful degree of discriminative power to detect misinformation. Finally, we discussed how WhatsApp misinformation detection approaches can be used in practice, highlighting challenges and opportunities.

WebMedia 2021

Detecção de Desinformação no WhatsApp: Uma Investigação do Potencial de Abordagens Supervisionadas

whatsapp

supervised learning

misinformation detection

### Welcome to the Brazilian Symposium on Multimedia Systems and Web 
Promoted annually by the Brazilian Computer Society (SBC), the Brazilian Symposium on Multimedia Systems and Web (WebMedia) is the main event on the subject in Brazil and an excellent opportunity for scientific and technical exchanges between students, researchers and professionals in the areas of Multimedia, Hypermedia and Web. This year, we will be celebrating the 27th edition of WebMedia, to be held from **November 5th to 12th, 2021**, organized by the Federal University of Minas Gerais and Federal University of São João Del Rei. For the second time in its history, the event will be held online, with interaction between the participants being done through tools and web platforms.
 <iframe width="560" height="315" src="https://www.youtube.com/embed/2GvWKRUDtAY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> 
The Webmedia 2021 schedule will feature:
 
* Technical sessions for the presentation of scientific articles.
* National and international lectures
* Panel with Companies
* Workshops
* Short courses
* Project meetings open to the community
 

The topics of interest for paper submission are:
 
* Web and Social Networks
* Multimedia
* Digital TV, Ubiquitous and Mobile Computing
 

In addition to the main track, WebMedia 2021 will feature the Tools and Applications Workshop, the Scientific Initiation Papers Contest, the Theses and Dissertations Contest, the RNP CT-Video Workshop.
 
The organization of WebMedia 2021 is under the responsibility of the [Department of Computer Science at UFMG (DCC/UFMG)](http://dcc.ufmg.br/) and [Department of Computer Science at UFSJ (DCOMP/UFSJ)](https://dcomp.ufsj.edu.br/)
 
**#PartiuWebMedia2021 #WebMedia2021**

The Brazilian Symposium on Multimedia Systems and Web (WebMedia) is the main event on the subject in Brazil and an excellent opportunity for scientific and technical exchanges between students, researchers and professionals in the areas of Multimedia, Hypermedia and Web.

technical paper

Em 2012, 56% das crianças entre 10 e 13 tinham um smartphone, atualmente, uma grande quantidade de dados são compartilhados a cada minuto na internet, inclusive em chats, isso acentua a necessidade de métodos automatizados de moderação de contaúdo. Esse trabalho propôe uma abordagem para controle parental tendo em mente a privacidade dos protegidos enquanto utiliza métodos com deep learning para detectar conteúdo sensível automaticamente.

Should I See or Should I Go?: Automatic Detection of Sensitive Media in Messaging Apps

Fake news can rapidly spread through internet users. Approaches proposed in the literature for content classification usually learn models considering textual and contextual features from real and fake news to minimize the spread of disinformation. One of the prominent approaches to detect fake news is One-Class Learning (OCL), as it minimizes the data labeling effort, requiring only the labeling of fake news documents. The performance of these algorithms depends on the structured representation of the documents used in the learning process. Generally, a textual-based unimodal representation is used, such as bag-of-words or representations based on linguistic categories. We propose MVAE-FakeNews, a multimodal representation method to detect fake news in OCL. The proposed approach uses a Multimodal Variational Autoencoder, learns a new representation from the combination of two modalities considered promising for fake news detection: text embeddings and topic information. In the experiments, we used three datasets considering Portuguese and English languages. Results show that the MVAE-FakeNews obtained a better F1-Score for the class of interest, outperforming another nine methods in ten of twelve evaluated scenarios. MVAE-FakeNews presented a better average ranking and statistical difference from other representation models. The proposed method proved to be promising to represent the texts in the OCL scenario to detect fake news.

Learning Textual Representations from Multiple Modalities to Detect Fake News Through One-Class Learning

The dissemination of news through digital media has amplified Fake News proliferation. In the face of this scenario, sentiment-based methods have presented promising results in Fake News detection. Although sentiment-based methods can extract sentiment (i.e., po- larity and/or emotion) from either texts or images available in news, the ones applied to Portuguese-written news have considered sen- timent exclusively extracted from texts. Thus, this study proposes a multimodal method that, besides the polarity and emotions ex- tracted from texts, also considers sentiment extracted from news’ images in order to detect Fake News written in Portuguese. The proposed method showed promising results in experimental data, overcoming the baseline methods in 8 p.p.

A Sentiment-Based Multimodal Method to Detect Fake News

A crucial task to overall video understanding is the recognition and localisation in time of different actions or events that are present along the scenes. To address this problem, action segmentation must be achieved. Action segmentation consists of temporally segmenting a video by labeling each frame with a specific action. In this work, we propose a novel action segmentation method that requires no prior video analysis and no annotated data. Our method involves extracting spatio-temporal features from videos using a pre-trained deep network. Data is then transformed using a positional encoder and finally a clustering algorithm is applied with the use of a silhouette score to find the optimal number of clusters where each cluster presumably corresponds to a different single and distinguishable action. In experiments, we show that our method produces competitive results on Breakfast and Inria Instructional Videos dataset benchmarks.

A Cluster-Based Method for Action Segmentation Using Spatio-Temporal and Positional Encoded Embeddings

Human pose estimation is an important field of Computer Vision that aims to predict poses of individuals from videos and images. It has been used in many different areas including human-computer interaction, motion analysis, surveillance, action prediction, action correction, augmented reality, virtual reality, and healthcare. This review is focused on the most significant contributions in human pose estimation for training assistance. Executing movements correctly is crucial in all kinds of physical activities, both to increase performance and reduce risk of injury. Human pose estimation is poised to help athletes better analyse the quality of their movements. 
The systematic review study was conducted in five databases including articles from January 2011 to March 2021. The initial search resulted in 129 articles, of which 8 were selected after applying the filtering criteria. Moreover this study presents the challenges related to pose estimation, which pose estimation methods have been used in recent years, in which specific activities the selected articles have focused on, and a taxonomy of human pose estimation methods.

Human Pose Estimation for Training Assistance: a Systematic Literature Review

Proposição de uma base de dados e um método de reconstrução de áudios temporalmente degradados.

Quality Enhancement of Highly Degraded Music Using Deep Learning-Based Prediction Models for Lost Frequencies

Determining the author’s intent in a social media post is a challeng-ing multimodal task and requires identifying complex relationshipsbetween image and text in the post. For example, the post imagecan represent an object, person, product, or company, while thetext can be an ironic message about the image content. Similarly, atext can be a news headline, while the image represents a provoca-tion, meme, or satire about the news. Existing approaches proposeintent classification techniques combining both modalities. How-ever, some posts may have missing textual annotations. Hence, weinvestigate a graph-based approach that propagates available textembedding data from complete multimodal posts to incompleteones. This paper presents a text embedding propagation method,which transfers embeddings from BERT neural language modelsto image-only posts (i.e., posts with incomplete modality) consid-ering the topology of a graph constructed from both visual andtextual modalities available during the training step. By using thisinference approach, our method provides competitive results whentextual modality is available at different completeness levels, evencompared to reference methods that require complete modalities.

Multimodal intent classification with incomplete modalities using text embedding propagation

GloboAB e GloboMAB: Fundamentando e acelerando decisões na Globo através de experimentação e otimização

Proposta de um modelo ontológico e serviços de captura e rastreamento de proveniência para redes sociais descentralizadas. Os resultados mostram que o modelo DOSN-PROV, desenvolvido foi verificado e validadado satisfatóriamente e os serviços apresentaram tempos de resposta aceitáveis para número de usuários e quantidade de triplas.

An Ontological Model and Services for Capturing and Tracking Provenance in Decentralized Social Networks

Instant messaging platforms such as Telegram and WhatsApp became one of the main means of communication used by people all over the world. In most of these services, communities are created around the so called groups and channels, allowing easy, encrypted and instantaneous information exchange. With the political debate gaining a widespread attention from the public and permeated with intense discussion and polarization, specially in a context in which far right communities are being banned from maistream social networks like Twitter, Youtube, and Facebook, alternative platforms, like Telegram become very popular as they start to be seeked as a ``free space to discussion'' and abused for dissemination of misinformation and hate speech. This work consists in a data analysis for Brazilian public groups and channels for political discussion on Telegram, observing the network created in the platform as well as a closer look in the dynamics of messages and members in this platform. Our findings show that political mobilization increased substantially on Telegram in recent years, suggesting a mass migration from other mainstream platforms. We find the large groups structure of Telegram are effective in spreading the messages through the network, with the content being viewed by numerous users and forwarded multiple times. Looking at the messages, we find an expressive interplay between Telegram and external web pages, notably for Youtube and other social networks. Furthermore, we observed a relevant amount of messages attacking political personalities and spreading unchecked content about COVID-19 pandemic. Taken all together, we perform an extense study in how political discussion advanced on Telegram in Brazil and how they operate in this alternative messaging application.

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