Gabriel Magno

Beware of What You Share: Inferring Home Location in Social Networks

In recent years, social media users are voluntarily making large volume of personal data available on the social networks. Such data (e.g., professional associations) can create opportunities for users to strengthen their social and professional ties. However, the same data can also be used against the user for viral marketing and other unsolicited purposes. The invasion of privacy occurs due to privacy unawareness and carelessness of making information publicly available. In this paper, we perform a large-scale inference study in three of the currently most popular social networks: Foursquare, Google+ and Twitter. Our work focuses on inferring a users home location, which may be a private attribute, for many users. We analyze whether a simple method can be used to infer the user home location using publicly available attributes and also the geographic information associated with locatable friends. We find that it is possible to infer the user home city with a high accuracy, around 67%, 72% and 82% of the cases in Foursquare, Google+ and Twitter, respectively. We also apply a finer-grained inference that reveals the geographic coordinates of the residence of a selected group of users in our datasets, achieving approximately up to 60% of accuracy within a radius of six kilometers.

Cross-Pollination of Information in Online Social Media: A Case Study on Popular Social Networks

Owing to the popularity of Online Social Media (OSM), Internet users share a lot of information on and across OSM services every day. Users recommend, comment, and forward information they receive from friends, contributing in spreading the information in and across OSM services. We term this information diffusion process from one OSM service to another as Cross-Pollination, and the network formed by users who participate in Cross-Pollination and content produced in the network as Cross-Pollinated network. Research has been done about information diffusion within one OSM service, but little is known about Cross-Pollination. We aim at filling this gap by studying how information from three popular OSM services (You Tube, Flickr and Foursquare) diffuses on Twitter, the most popular microblogging service. Our results show that Cross-Pollinated networks follow temporal and topological characteristics of Twitter. Furthermore, popularity of information on source OSM (You Tube, Flickr and Foursquare) does not imply its popularity on Twitter.

International gender differences and gaps in online social networks

Article 1 of the United Nations Charter claims “human rights” and “fundamental freedoms” “without distinction as to [...] sex”. Yet in 1995 the Human Development Report came to the sobering conclusion that “in no society do women enjoy the same opportunities as men”. Today, gender disparities remain a global issue and addressing them is a top priority for organizations such as the United Nations Population Fund. To track progress in this matter and to observe the effect of new policies, the World Economic Forum annually publishes its Global Gender Gap Report. This report is based on a number of offline variables such as the ratio of female-to-male earned income or the percentage of women in executive office over the last 50 years.

Noticing the other gender on Google

Google+ provides a feature that has been overlooked in social media studies: the possibility of users setting their gender information not only as female or male, but as other instead. In this paper, we discuss this particularity and, more broadly, the issue of non-binary gender roles in the Web. By analyzing a large dataset, we characterize some aspects of self presentation, word use, network information and country of residence among users who choose different alternatives in the field Gender. On the whole, our main contributions are to present preliminary results and to shed light into the topic considered here - namely, the implications of having a third gender option to present oneself in an online social networking service.

The Right to be Forgotten in the Media: A Data-Driven Study

Abstract Due to the recent “Right to be Forgotten” (RTBF) ruling, for queries about an individual, Google and other search engines now delist links to web pages that contain “inadequate, irrelevant or no longer relevant, or excessive” information about that individual. In this paper we take a data-driven approach to study the RTBF in the traditional media outlets, its consequences, and its susceptibility to inference attacks. First, we do a content analysis on 283 known delisted UK media pages, using both manual investigation and Latent Dirichlet Allocation (LDA). We find that the strongest topic themes are violent crime, road accidents, drugs, murder, prostitution, financial misconduct, and sexual assault. Informed by this content analysis, we then show how a third party can discover delisted URLs along with the requesters’ names, thereby putting the efficacy of the RTBF for delisted media links in question. As a proof of concept, we perform an experiment that discovers two previously-unknown delisted URLs and their corresponding requesters. We also determine 80 requesters for the 283 known delisted media pages, and examine whether they suffer from the “Streisand effect,” a phenomenon whereby an attempt to hide a piece of information has the unintended consequence of publicizing the information more widely. To measure the presence (or lack of presence) of a Streisand effect, we develop novel metrics and methodology based on Google Trends and Twitter data. Finally, we carry out a demographic analysis of the 80 known requesters. We hope the results and observations in this paper can inform lawmakers as they refine RTBF laws in the future.

Brazil Around the World: Characterizing and Detecting Brazilian Emigrants Using Google+

Currently available data about people whose left their home country to live in a foreign country does not adequately capture the standards of contemporary global migration flows. A new trend for migration studies is to study the data from the Internet, either by Social Networks or other data in the WEB. In this study, we collected users data from the social network Google+ to investigate which features of Brazilian users are relevant to classify them as a possible emigrant. Our study uses machine learning techniques, SVM. We selected some features to compose our dataset. Our results show that the network features were the ones that had greater capacity for discrimination. The most relevant for the prediction of Brazilian emigrants users are, in order: reciprocity, PageRank, in-degree, clustering coefficient and ratio of incoming foreigners.

The Social Name-Letter Effect on Online Social Networks

The Name-Letter Effect states that people have a preference for brands, places, and even jobs that start with the same letter as their own first name. So Sam might like Snickers and live in Seattle. We use social network data from Twitter and Google+ to replicate this effect in a new environment. We find limited to no support for the Name-Letter Effect on social networks. We do, however, find a very robust Same-Name Effect where, say, Michaels would be more likely to link to other Michaels than Johns. This effect persists when accounting for gender, nationality, race, and age. The fundamentals behind these effects have implications beyond psychology as understanding how a positive self-image is transferred to other entities is important in domains ranging from studying homophily to personalized advertising and to link formation in social networks.

Fake News as We Feel It: Perception and Conceptualization of the Term “Fake News” in the Media

In this article, we quantitatively analyze how the term “fake news” is being shaped in news media in recent years. We study the perception and the conceptualization of this term in the traditional media using eight years of data collected from news outlets based in 20 countries. Our results not only corroborate previous indications of a high increase in the usage of the expression “fake news”, but also show contextual changes around this expression after the United States presidential election of 2016. Among other results, we found changes in the related vocabulary, in the mentioned entities, in the surrounding topics and in the contextual polarity around the term “fake news”, suggesting that this expression underwent a change in perception and conceptualization after 2016. These outcomes expand the understandings on the usage of the term “fake news”, helping to comprehend and more accurately characterize this relevant social phenomenon linked to misinformation and manipulation.

Characterizing Videos, Audience and Advertising in Youtube Channels for Kids

Online video services, messaging systems, games and social media services are tremendously popular among young people and children in many countries. Most of the digital services offered on the internet are advertising funded, which makes advertising ubiquitous in childrens everyday life. To understand the impact of advertising-based digital services on children, we study the collective behavior of users of YouTube for kids channels and present the demographics of a large number of users. We collected data from 12,848 videos from 17 channels in US and UK and 24 channels in Brazil. The channels in English have been viewed more than 37 billion times. We also collected more than 14 million comments made by users. Based on a combination of text-analysis and face recognition tools, we show the presence of racial and gender biases in our large sample of users. We also identify children actively using YouTube, although the minimum age for using the service is 13 years in most countries. We provide comparisons of user behavior among the three countries, which represent large user populations in the global North and the global South.