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Publications

Publications by Álvaro Figueira

2018

Analysis and Detection of Unreliable Users in Twitter: Two Case Studies

Authors
Guimarães, N; Figueira, A; Torgo, L;

Publication
Knowledge Discovery, Knowledge Engineering and Knowledge Management - 10th International Joint Conference, IC3K 2018, Seville, Spain, September 18-20, 2018, Revised Selected Papers

Abstract

2021

Profiling Accounts Political Bias on Twitter

Authors
Guimaraes, N; Figueira, A; Torgo, L;

Publication
PROCEEDINGS OF 2021 16TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI'2021)

Abstract
Twitter has become a major platform to share ideas and promoting discussion on relevant topics. However, with a large number of users to resort to it as their primary source of information and with an increasing number of accounts spreading newsworthy content, a characterization of the political bias associated with the social network ecosystem becomes necessary. In this work, we aim at analyzing accounts spreading or publishing content from five different classes of the political spectrum. We also look further and study accounts who spread content from both right and left sides. Conclusions show that there is a large presence of accounts which disseminate right bias content although it is the more central classes that have a higher influence on the network. In addition, users who spread content from both sides are more actively spreading right content with opposite content associated with criticism towards left political parties or promoting right political decisions.

2021

Towards a pragmatic detection of unreliable accounts on social networks

Authors
Guimarães, N; Figueira, A; Torgo, L;

Publication
Online Soc. Networks Media

Abstract
In recent years, the problem of unreliable content in social networks has become a major threat, with a proven real-world impact in events like elections and pandemics, undermining democracy and trust in science, respectively. Research in this domain has focused not only on the content but also on the accounts that propagate it, with the bot detection task having been thoroughly studied. However, not all bot accounts work as unreliable content spreaders (p.e. bot for news aggregation), and not all human accounts are necessarily reliable. In this study, we try to distinguish unreliable from reliable accounts, independently of how they are operated. In addition, we work towards providing a methodology capable of coping with real-world situations by introducing the content available (restricting it by volume- and time-based batches) as a parameter of the methodology. Experiments conducted on a validation set with a different number of tweets per account provide evidence that our proposed solution produces an increase of up to 20% in performance when compared with traditional (individual) models and with cross-batch models (which perform better with different batches of tweets).

2020

Knowledge-based Reliability Metrics for Social Media Accounts

Authors
Guimaraes, N; Figueira, A; Torgo, L;

Publication
PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND TECHNOLOGIES (WEBIST)

Abstract
The growth of social media as an information medium without restrictive measures on the creation of new accounts led to the rise of malicious agents with the intend to diffuse unreliable information in the network, ultimately affecting the perception of users in important topics such as political and health issues. Although the problem is being tackled within the domain of bot detection, the impact of studies in this area is still limited due to 1) not all accounts that spread unreliable content are bots, 2) human-operated accounts are also responsible for the diffusion of unreliable information and 3) bot accounts are not always malicious (e.g. news aggregators). Also, most of these methods are based on supervised models that required annotated data and updates to maintain their performance through time. In this work, we build a framework and develop knowledge-based metrics to complement the current research in bot detection and characterize the impact and behavior of a Twitter account, independently of the way it is operated (human or bot). We proceed to analyze a sample of the accounts using the metrics proposed and evaluate the necessity of these metrics by comparing them with the scores from a bot detection system. The results show that the metrics can characterize different degrees of unreliable accounts, from unreliable bot accounts with a high number of followers to human-operated accounts that also spread unreliable content (but with less impact on the network). Furthermore, evaluating a sample of the accounts with a bot detection system shown that bots compose around 11% of the sample of unreliable accounts extracted and that the bot score is not correlated with the proposed metrics. In addition, the accounts that achieve the highest values in our metrics present different characteristics than the ones that achieve the highest bot score. This provides evidence on the usefulness of our metrics in the evaluation of unreliable accounts in social networks. Copyright

2021

Covid-19 Impact on Higher Education Institution's Social Media Content Strategy

Authors
Coelho, T; Figueira, A;

Publication
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT II

Abstract
In recent years we have seen a large adherence to social media by various Higher Education Institutions (HEI) with the intent of reaching their target audiences and improve their public image. These institutional publications are guided by a specific editorial strategy, designed to help them better accomplish and fulfill their mission. The current Covid-19 pandemic has had major consequences in many different fields (political, economic, social, educational) beyond the spread of the disease itself. In this paper, we attempt to determine the impact of the pandemic on the HEI content strategies by gauging if these social-economical, cultural and psychological changes that occurred during this global catastrophe are actively reflected in their publications. Furthermore, we identified the topics that emerge from the pandemic situation checking the trend changes and the concept drift that many topics had. We gathered and analyzed more than 18k Twitter publications from 12 of the top HEI according to the 2019 Center for World University Rankings (CWUR). Utilizing machine learning techniques, and topic modeling, we determined the emergent content topics for each institution before, and during, the Covid-19 pandemic to uncover any significant differences in the strategies.

2021

A Mixed Model for Identifying Fake News in Tweets from the 2020 US Presidential Election

Authors
Bernardes, V; Figueira, A;

Publication
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND TECHNOLOGIES (WEBIST)

Abstract
The recent proliferation of so called fake news content, assisted by the widespread use of social media platforms and with serious real-world impacts, makes it imperative to find ways to mitigate this problem. In this paper we propose a machine learning-based approach to tackle it by automatically identifying tweets associated with questionable content, using newly-collected data from Twitter about the 2020 U.S. presidential election. To create a sizable annotated data set, we use an automatic labeling process based on the factual reporting level of links contained in tweets, as classified by human experts. We derive relevant features from that data and investigate the specific contribution of features derived from named entity and emotion recognition techniques, including a novel approach using sequences of prevalent emotions. We conclude the paper by evaluating and comparing the performance of several machine learning models on different test sets, and show they are applicable to addressing the issue of fake news dissemination.

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