Nearly half of Brazil's 180 Indigenous languages face extinction within the next 20 years. What's more concerning is that most of these languages lack a single scientific article describing them, which means they could disappear without leaving any documented evidence of their existence. This work investigates the state of articles about those languages in Wikipedia, both in the English and Portuguese versions, regarded here as indicative of the minimum world-level trace of the previous existence of these languages. Our study shows that over 30% of these languages do not have a single Wikipedia article describing them. It also highlights that the Portuguese and English editing communities are not only distinct, but have different practices, achieving similar levels of quality through different temporal dynamics. These results, although encouraging, suggest that any effort to enhance coverage comprehensiveness in both Wikipedias should consider different strategies for engaging each editing community.

In this paper we discuss how AI can contribute to support the documentation and vitalization of Indigenous languages and how that involves a delicate balancing of ensuring social impact, exploring technical opportunities, and dealing with ethical constraints. We start by surveying previous work on using AI and NLP to support critical activities of strengthening Indigenous and endangered languages and discussing key limitations of current technologies. After presenting basic ethical constraints of working with Indigenous languages and communities, we propose that creating and deploying language technology ethically with and for Indigenous communities forces AI researchers and engineers to address some of the main shortcomings and criticisms of current technologies. Those ideas are also explored in the discussion of a real case of development of large language models for Brazilian Indigenous languages.

Chatbots have received significant attention in the last years. These systems have improved operational efficiency by reducing the cost of customer service and more and more are used in customer service channels. More recently, with the addition of speech capabilities added to those systems, bot language modifications have to be done in order to improve user experience. In this paper, we analyze the language used in those systems using datasets collected from real chatbots. For that, we first propose models that are able to identify the language style (writing, speech, and computer-mediated) using as linguist features syntactic (Biber's dimensions) and sentence embeddings as well as state-of-art datasets. Our results show that our models were able to distinguish among the three classes with an accuracy of up to 66%. Finally, we evaluate real chatbots systems, either speech- and text-based ones, using our proposed models. We found that these real chatbots are generally using a computer-mediated style, but there is indication that their developers tend to adapt the language style to be more speech-like when text-to-speech is used.

As Internet users increasingly rely on social media sites to receive news, they are faced with a bewildering number of news media choices. For example, thousands of Facebook pages today are registered and categorized as some form of news media outlets. This situation boosted the so-called independent journalism, also known as alternative news media. Identifying and characterizing all the news pages that play an important role in news dissemination is key for understanding the news ecosystems of a country. In this work, we propose a graph-based semi-supervised method to measure the political bias of pages on most countries and show the political split of the alternative media, mainstream media, and public figures pages. We validate our method using the publicly available U.S. dataset and then apply it to Brazilian pages, where we found a larger number of right-wing pages in general, except for alternative news media.

We present a methodology to use Twitter posts to create a parallel corpus which can be used to train Seq2Seq neural networks for a tone rephrasing task. Given that people tend to post texts expressing opinions or emotions of varied intensities regarding given real-world events, the main idea is to create corpus containing pairs of posts with opposite tone but about the same topic. By doing so we overcome the main limitation of current tone rephrasing methods: the lack of appropriate parallel training corpora. We explore different methods to create the datasets, including some which require some level of manual labelling. The results show that a completely automatic generation from Twitter data yields training datasets which are better than those with manual interventions, and good enough for Seq2Seq models to outperform non-Seq2Seq models trained with similar data.

WhatsApp is a free messaging app with more than one billion active monthly users which has become one of the main communication platforms in many countries, including Saudi Arabia, Germany, and Brazil. In addition to allowing the direct exchange of messages among pairs of users, the app also enables group conversations, where multiple people can interact with one another. A number of recent studies have shown that WhatsApp groups play an important role as an information dissemination platform, especially during important social mobilization events. In this paper, we build upon those prior efforts by taking a first look into the use of audio messages in WhatsApp groups, a type of content that is becoming increasingly important in the platform. We present a methodology to analyze audio messages shared in WhatsApp groups, characterizing content properties (e.g, topics and language characteristics), their propagation dynamics and the impact of different types of audios (e.g., speech versus music) on such dynamics.

How does the presence of an audience influence the social interaction with a conversational system in a physical space? To answer this question, we analyzed data from an art exhibit where visitors interacted in natural language with three chatbots representing characters from a book. We performed two studies to explore the influence of audiences. In Study 1, we did fieldwork cross-analyzing the reported perception of the social interaction, the audience conditions (visitor is alone, visitor is observed by acquaintances and/or strangers), and control variables such as the visitor's familiarity with the book and gender. In Study 2, we analyzed over 5,000 conversation logs and video recordings, identifying dialogue patterns and how they correlated with the audience conditions. Some significant effects were found, suggesting that conversational systems in physical spaces should be designed based on whether other people observe the user or not.

WhatsApp has revolutionized the way people communicate and interact. It is not only cheaper than the traditional Short Message Service (SMS) communication but it also brings a new form of mobile communication: the group chats. Such groups are great forums for collective discussions on a variety of topics. In particular, in events of great social mobilization, such as strikes and electoral campaigns, WhatsApp group chats are very attractive as they facilitate information exchange among interested people. Yet, recent events have raised concerns about the spreading of misinformation in WhatsApp. In this work, we analyze information dissemination within WhatsApp, focusing on publicly accessible political-oriented groups, collecting all shared messages during major social events in Brazil: a national truck drivers' strike and the Brazilian presidential campaign. We analyze the types of content shared within such groups as well as the network structures that emerge from user interactions within and cross-groups. We then deepen our analysis by identifying the presence of misinformation among the shared images using labels provided by journalists and by a proposed automatic procedure based on Google searches. We identify the most important sources of the fake images and analyze how they propagate across WhatsApp groups and from/to other Web platforms.

Artificial Intelligence (AI) has been used extensively in automatic decision making in a broad variety of scenarios, ranging from credit ratings for loans to recommendations of movies. Traditional design guidelines for AI models focus essentially on accuracy maximization, but recent work has shown that economically irrational and socially unacceptable scenarios of discrimination and unfairness are likely to arise unless these issues are explicitly addressed. This undesirable behavior has several possible sources, such as biased datasets used for training that may not be detected in black-box models. After pointing out connections between such bias of AI and the problem of induction, we focus on Popper's contributions after Hume's, which offer a logical theory of preferences. An AI model can be preferred over others on purely rational grounds after one or more attempts at refutation based on accuracy and fairness. Inspired by such epistemological principles, this paper proposes a structured approach to mitigate discrimination and unfairness caused by bias in AI systems. In the proposed computational framework, models are selected and enhanced after attempts at refutation. To illustrate our discussion, we focus on hiring decision scenarios where an AI system filters in which job applicants should go to the interview phase.

In the last few years, the increasing interest in location-based services (LBS) has favored the introduction of geo-referenced information in various Web 2.0 applications, as well as the rise of location-based social networks (LBSN). Foursquare, one of the most popular LBSNs, gives incentives to users who visit (check in) specific places (venues) by means of, for instance, mayorships to frequent visitors. Moreover, users may leave tips at specific venues as well as mark previous tips as done in sign of agreement. Unlike check ins, which are shared only with friends, the lists of mayorships, tips and dones of a user are publicly available to everyone, thus raising concerns about disclosure of the user's movement patterns and interests. We analyze how users explore these publicly available features, and their potential as sources of information leakage. Specifically, we characterize the use of mayorships, tips and dones in Foursquare based on a dataset with around 13 million users. We also analyze whether it is possible to easily infer the home city (state and country) of a user from these publicly available information. Our results indicate that one can easily infer the home city of around 78% of the analyzed users within 50 kilometers.

Online Location Based Social Networks (LBSNs), which combine social network features with geographic information sharing, are becoming increasingly popular. One such application is Foursquare, which doubled its user population in less than six months. Among other features, Foursquare allows users to leave tips (i.e., reviews or recommendations) at specific venues as well as to give feedback on previously posted tips by adding them to their to-do lists or marking them as done. In this paper, we analyze how Foursquare users exploit these three features - tips, dones and to-dos - uncovering different behavior profiles. Our study reveals the existence of very active and influential users, some of which are famous businesses and brands, that seem engaged in posting tips at a large variety of venues while also receiving a great amount of user feedback on them. We also provide evidence of spamming, showing the existence of users that post tips whose contents are unrelated to the nature or domain of the venue where the tips were left.