F
Fabrício Benevenuto
Researcher at Universidade Federal de Minas Gerais
Publications - 207
Citations - 11958
Fabrício Benevenuto is an academic researcher from Universidade Federal de Minas Gerais. The author has contributed to research in topics: Social media & Sentiment analysis. The author has an hindex of 45, co-authored 189 publications receiving 10194 citations. Previous affiliations of Fabrício Benevenuto include Max Planck Society & Universidade Federal de Ouro Preto.
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Proceedings Article
Measuring User Influence in Twitter: The Million Follower Fallacy
TL;DR: An in-depth comparison of three measures of influence, using a large amount of data collected from Twitter, is presented, suggesting that topological measures such as indegree alone reveals very little about the influence of a user.
Proceedings ArticleDOI
Characterizing user behavior in online social networks
TL;DR: A first of a kind analysis of user workloads in online social networks, based on detailed clickstream data collected over a 12-day period, shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities.
Proceedings ArticleDOI
Understanding and combating link farming in the twitter social network
Saptarshi Ghosh,Bimal Viswanath,Farshad Kooti,Naveen Kumar Sharma,Gautam Korlam,Fabrício Benevenuto,Niloy Ganguly,Krishna P. Gummadi +7 more
TL;DR: It is shown that a simple user ranking scheme that penalizes users for connecting to spammers can effectively address the link farming problem in Twitter by disincentivizing users from linking with other users simply to gain influence.
Proceedings ArticleDOI
Comparing and combining sentiment analysis methods
TL;DR: A new method that combines existing approaches, providing the best coverage results and competitive agreement is developed and a free Web service called iFeel is presented, which provides an open API for accessing and comparing results across different sentiment methods for a given text.
Journal ArticleDOI
Supervised Learning for Fake News Detection
Julio Cesar dos Reis,Andre Correia,Fabricio Murai,Adriano Veloso,Fabrício Benevenuto,Erik Cambria +5 more
TL;DR: A new set of features is presented and the prediction performance of current approaches and features for automatic detection of fake news are measured, revealing interesting findings on the usefulness and importance of features for detecting false news.