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Characterizing Interconnections and Linguistic Patterns in Twitter [article]

Johnnatan Messias
2018 arXiv   pre-print
Messias et al. [2017] • Vikatos, P., Messias, J., Miranda, M., and Benevenuto, F. (2017). Linguistic Diversities of Demographic Groups in Twitter.  ...  • Kulshrestha, J., Eslami, M., Messias, J., Zafar, M. B., Ghosh, S., Gummadi, K. P., and Karahalios, K. (2017).  ... 
arXiv:1804.00084v1 fatcat:pjwacsyxgrc3hnm7gtyl2dlpaa

Who Makes Trends? Understanding Demographic Biases in Crowdsourced Recommendations [article]

Abhijnan Chakraborty, Johnnatan Messias, Fabricio Benevenuto, Saptarshi Ghosh, Niloy Ganguly, Krishna P. Gummadi
2017 arXiv   pre-print
Users of social media sites like Facebook and Twitter rely on crowdsourced content recommendation systems (e.g., Trending Topics) to retrieve important and useful information. Contents selected for recommendation indirectly give the initial users who promoted (by liking or posting) the content an opportunity to propagate their messages to a wider audience. Hence, it is important to understand the demographics of people who make a content worthy of recommendation, and explore whether they are
more » ... resentative of the media site's overall population. In this work, using extensive data collected from Twitter, we make the first attempt to quantify and explore the demographic biases in the crowdsourced recommendations. Our analysis, focusing on the selection of trending topics, finds that a large fraction of trends are promoted by crowds whose demographics are significantly different from the overall Twitter population. More worryingly, we find that certain demographic groups are systematically under-represented among the promoters of the trending topics. To make the demographic biases in Twitter trends more transparent, we developed and deployed a Web-based service 'Who-Makes-Trends' at
arXiv:1704.00139v1 fatcat:tdv6qsumabbbppn7xuhsjfs75e

You followed my bot! Transforming robots into influential users in Twitter

Johnnatan Messias, Lucas Schmidt, Ricardo Oliveira, Fabrício Benevenuto
2013 First Monday  
it is possible to become influential using very simple strategies, suggesting that these systems should review their influence score algorithms to avoid accounting with automatic activity. 21/01/2015 Messias  ...  Messias 11/14 21/01/2015 doi:10.5210/fm.v18i7.4217  ...  These scenarios test the vulnerability of influence classification methods and how easily they can be manipulated. 21/01/2015 Messias  ... 
doi:10.5210/fm.v18i7.4217 fatcat:i4ucdjczebg7fc6ceunfvnvp7y

From Migration Corridors to Clusters: The Value of Google+ Data for Migration Studies [article]

Johnnatan Messias and Fabricio Benevenuto and Ingmar Weber and Emilio Zagheni
2016 arXiv   pre-print
Recently, there have been considerable efforts to use online data to investigate international migration. These efforts show that Web data are valuable for estimating migration rates and are relatively easy to obtain. However, existing studies have only investigated flows of people along migration corridors, i.e. between pairs of countries. In our work, we use data about "places lived" from millions of Google+ users in order to study migration "clusters", i.e. groups of countries in which
more » ... duals have lived. For the first time, we consider information about more than two countries people have lived in. We argue that these data are very valuable because this type of information is not available in traditional demographic sources which record country-to-country migration flows independent of each other. We show that migration clusters of country triads cannot be identified using information about bilateral flows alone. To demonstrate the additional insights that can be gained by using data about migration clusters, we first develop a model that tries to predict the prevalence of a given triad using only data about its constituent pairs. We then inspect the groups of three countries which are more or less prominent, compared to what we would expect based on bilateral flows alone. Next, we identify a set of features such as a shared language or colonial ties that explain which triple of country pairs are more or less likely to be clustered when looking at country triples. Then we select and contrast a few cases of clusters that provide some qualitative information about what our data set shows. The type of data that we use is potentially available for a number of social media services. We hope that this first study about migration clusters will stimulate the use of Web data for the development of new theories of international migration that could not be tested appropriately before.
arXiv:1607.00421v1 fatcat:nqm5enmspzhpdfxvaky2d5mybe


Johnnatan Messias, Lucas Schmidt, Ricardo Oliveira, Fabricio Benevenuto
2015 Revista Eletrônica de Sistemas de Informação  
Messias Departamento de Ciência da Computação - Universidade Federal de Minas Gerais (UFMG) Lucas Schmidt Departamento de Computação -- Universidade Federal de Ouro Preto  ...  Messias et al. (2012) e Messias et al. (2013) apresentam experimentos com bots no Twitter que comprovam a vulnerabilidade desses sistemas, 3 CONSTRUÇÃO DOS BOTS Após o processo de execução, acessamos  ... 
doi:10.21529/resi.2015.1402004 fatcat:ibl3s7244vfkzhycv7iu74mwpy

Search bias quantification: investigating political bias in social media and web search

Juhi Kulshrestha, Motahhare Eslami, Johnnatan Messias, Muhammad Bilal Zafar, Saptarshi Ghosh, Krishna P. Gummadi, Karrie Karahalios
2018 Information retrieval (Boston)  
Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives.
doi:10.1007/s10791-018-9341-2 fatcat:st7rnexr3nby5jboqnry6xhxp4

White, man, and highly followed

Johnnatan Messias, Pantelis Vikatos, Fabrício Benevenuto
2017 Proceedings of the International Conference on Web Intelligence - WI '17  
Johnnatan Messias, Pantelis Vikatos, and Fabrício Benevenuto In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI'17). Leipzig, Germany. August 2017.  ... 
doi:10.1145/3106426.3106472 dblp:conf/webi/MessiasVB17 fatcat:xsxyxh2eojf73now4zimsrobtq

Linguistic Diversities of Demographic Groups in Twitter

Pantelis Vikatos, Johnnatan Messias, Manoel Miranda, Fabrício Benevenuto
2017 Proceedings of the 28th ACM Conference on Hypertext and Social Media - HT '17  
The massive popularity of online social media provides a unique opportunity for researchers to study the linguistic characteristics and patterns of user's interactions. In this paper, we provide an in-depth characterization of language usage across demographic groups in Twitter. In particular, we extract the gender and race of Twitter users located in the U.S. using advanced image processing algorithms from Face++. Then, we investigate how demographic groups (i.e. male/female,
more » ... differ in terms of linguistic styles and also their interests. We extract linguistic features from 6 categories (affective attributes, cognitive attributes, lexical density and awareness, temporal references, social and personal concerns, and interpersonal focus), in order to identify the similarities and differences in particular writing set of attributes. In addition, we extract the absolute ranking difference of top phrases between demographic groups. As a dimension of diversity, we also use the topics of interest that we retrieve from each user. Our analysis unveils clear differences in the writing styles (and the topics of interest) of different demographic groups, with variation seen across both gender and race lines. We hope our effort can stimulate the development of new studies related to demographic information in the online space.
doi:10.1145/3078714.3078742 dblp:conf/ht/VikatosMMB17 fatcat:hlycem6uhjeqjorioiaidfi3yu

Demographics of News Sharing in the U.S. Twittersphere

Julio C.S. Reis, Haewoon Kwak, Jisun An, Johnnatan Messias, Fabrício Benevenuto
2017 Proceedings of the 28th ACM Conference on Hypertext and Social Media - HT '17  
Reis, Haewoon Kwak, Jisun An, Johnnatan Messias, and Fabrício Benevenuto In Proceedings of the 28th ACM Conference on Hypertext and Social Media (HT'17). Prague, Czech Republic. July 2017.  ... 
doi:10.1145/3078714.3078734 dblp:conf/ht/ReisKAMB17 fatcat:caosfigtdfdidmcza4zzekfx3y

Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media [article]

Juhi Kulshrestha, Motahhare Eslami, Johnnatan Messias, Muhammad Bilal Zafar, Saptarshi Ghosh, Krishna P. Gummadi, Karrie Karahalios
2017 arXiv   pre-print
Search systems in online social media sites are frequently used to find information about ongoing events and people. For topics with multiple competing perspectives, such as political events or political candidates, bias in the top ranked results significantly shapes public opinion. However, bias does not emerge from an algorithm alone. It is important to distinguish between the bias that arises from the data that serves as the input to the ranking system and the bias that arises from the
more » ... g system itself. In this paper, we propose a framework to quantify these distinct biases and apply this framework to politics-related queries on Twitter. We found that both the input data and the ranking system contribute significantly to produce varying amounts of bias in the search results and in different ways. We discuss the consequences of these biases and possible mechanisms to signal this bias in social media search systems' interfaces.
arXiv:1704.01347v1 fatcat:f7oaysnuqjhxnnq67oczypwcd4

Bots Sociais: Como robôs podem se tornar pessoas influentes no Twitter?

Johnnatan Messias, Fabrício Benevenuto, Ricardo Oliveira
Este trabalho foi realizado pelo aluno Johnnatan Messias, estudante de Ciência da Computação na Universidade Federal de Ouro Preto e responsável por todas as etapas de sua execução.  ... 

On Microtargeting Socially Divisive Ads

Filipe N. Ribeiro, Koustuv Saha, Mahmoudreza Babaei, Lucas Henrique, Johnnatan Messias, Fabricio Benevenuto, Oana Goga, Krishna P. Gummadi, Elissa M. Redmiles
2019 Proceedings of the Conference on Fairness, Accountability, and Transparency - FAT* '19  
Messias Oana Goga UFMG, Brazil MPI-SWS, Germany University  ... Lucas Henrique Johnnatan  ... 
doi:10.1145/3287560.3287580 dblp:conf/fat/RibeiroSBHMBGGR19 fatcat:5qkpqgz65bghleo6qhju3y4tim

Selfish Opaque Transaction Ordering in the Bitcoin Blockchain: The Case for Chain Neutrality [article]

Johnnatan Messias, Mohamed Alzayat, Balakrishnan Chandrasekaran, Krishna P. Gummadi, Patrick Loiseau, Alan Mislove
2021 pre-print
Most public blockchain protocols, including the popular Bitcoin and Ethereum blockchains, do not formally specify the order in which miners should select transactions from the pool of pending (or uncommitted) transactions for inclusion in the blockchain. Over the years, informal conventions or "norms" for transaction ordering have, however, emerged via the use of shared software by miners, e.g., the GetBlockTemplate (GBT) mining protocol in Bitcoin Core. Today, a widely held view is that
more » ... miners prioritize transactions based on their offered "transaction fee-per-byte." Bitcoin users are, consequently, encouraged to increase the fees to accelerate the commitment of their transactions, particularly during periods of congestion. In this paper, we audit the Bitcoin blockchain and present statistically significant evidence of mining pools deviating from the norms to accelerate the commitment of transactions for which they have (i) a selfish or vested interest, or (ii) received dark-fee payments via opaque (non-public) side-channels. As blockchains are increasingly being used as a record-keeping substrate for a variety of decentralized (financial technology) systems, our findings call for an urgent discussion on defining neutrality norms that miners must adhere to when ordering transactions in the chains. Finally, we make our data sets and scripts publicly available.
doi:10.1145/3487552.3487823 arXiv:2110.11740v1 fatcat:h3lis5ss6vhx7dvaskqxorcvpy

Bazinga! Caracterizando e Detectando Sarcasmo e Ironia no Twitter

Pollyanna Gonçalves, Daniel Dalip, Julio Reis, Johnnatan Messias, Filipe Ribeiro, Philipe Melo, Leandro Araújo, Marcos Gonçalves, Fabricio Benevenuto
2015 Anais do Brazilian Workshop on Social Network Analysis and Mining (BraSNAM)   unpublished
{pollyannaog,hasan,julio.reis,johnnatan,philipe} {leandroaraujo,fabricio,mgoncalv} {filipe} Abstract.  ... 
doi:10.5753/brasnam.2015.6778 fatcat:6z76d6yqb5gofcoiawzilmz2s4

Migration Multiple? Big Data, Knowledge Practices and the Governability of Migration [chapter]

Laura Stielike
2021 Research Methodologies and Ethical Challenges in Digital Migration Studies  
In their paper, Johnnatan Messias et al. draw on data from Google+ profiles to study migration clusters-the relocation of a person between three countries.  ...  We refer to this group of users as migrants " (Messias et al. 2016, 423, emphasis in original) . This means that people are considered as migrants if they have ever lived in more than one country.  ... 
doi:10.1007/978-3-030-81226-3_5 fatcat:alcmc356wbdazobp6mv7vtseuu
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