Fabrício Benevenuto

The tube over time: characterizing popularity growth of youtube videos

Understanding content popularity growth is of great importance to Internet service providers, content creators and online marketers. In this work, we characterize the growth patterns of video popularity on the currently most popular video sharing application, namely YouTube. Using newly provided data by the application, we analyze how the popularity of individual videos evolves since the videos upload time. Moreover, addressing a key aspect that has been mostly overlooked by previous work, we characterize the types of the referrers that most often attracted users to each video, aiming at shedding some light into the mechanisms (e.g., searching or external linking) that often drive users towards a video, and thus contribute to popularity growth. Our analyses are performed separately for three video datasets, namely, videos that appear in the YouTube top lists, videos removed from the system due to copyright violation, and videos selected according to random queries submitted to YouTubes search engine. Our results show that popularity growth patterns depend on the video dataset. In particular, copyright protected videos tend to get most of their views much earlier in their lifetimes, often exhibiting a popularity growth characterized by a viral epidemic-like propagation process. In contrast, videos in the top lists tend to experience sudden significant bursts of popularity. We also show that not only search but also other YouTube internal mechanisms play important roles to attract users to videos in all three datasets.

Detecting spammers and content promoters in online video social networks

A number of online video social networks, out of which YouTube is the most popular, provides features that allow users to post a video as a response to a discussion topic. These features open opportunities for users to introduce polluted content, or simply pollution, into the system. For instance, spammers may post an unrelated video as response to a popular one aiming at increasing the likelihood of the response being viewed by a larger number of users. Moreover, opportunistic users--promoters--may try to gain visibility to a specific video by posting a large number of (potentially unrelated) responses to boost the rank of the responded video, making it appear in the top lists maintained by the system. Content pollution may jeopardize the trust of users on the system, thus compromising its success in promoting social interactions. In spite of that, the available literature is very limited in providing a deep understanding of this problem. In this paper, we go a step further by addressing the issue of detecting video spammers and promoters. Towards that end, we manually build a test collection of real YouTube users, classifying them as spammers, promoters, and legitimates. Using our test collection, we provide a characterization of social and content attributes that may help distinguish each user class. We also investigate the feasibility of using a state-of-the-art supervised classification algorithm to detect spammers and promoters, and assess its effectiveness in our test collection. We found that our approach is able to correctly identify the majority of the promoters, misclassifying only a small percentage of legitimate users. In contrast, although we are able to detect a significant fraction of spammers, they showed to be much harder to distinguish from legitimate users.

Comparing and combining sentiment analysis methods

Several messages express opinions about events, products, and services, political views or even their authors emotional state and mood. Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services, and simply to better understand aspects of social communication in Online Social Networks (OSNs). There are multiple methods for measuring sentiments, including lexical-based approaches and supervised machine learning methods. Despite the wide use and popularity of some methods, it is unclear which method is better for identifying the polarity (i.e., positive or negative) of a message as the current literature does not provide a method of comparison among existing methods. Such a comparison is crucial for understanding the potential limitations, advantages, and disadvantages of popular methods in analyzing the content of OSNs messages. Our study aims at filling this gap by presenting comparisons of eight popular sentiment analysis methods in terms of coverage (i.e., the fraction of messages whose sentiment is identified) and agreement (i.e., the fraction of identified sentiments that are in tune with ground truth). We develop a new method that combines existing approaches, providing the best coverage results and competitive agreement. We also present a free Web service called iFeel, which provides an open API for accessing and comparing results across different sentiment methods for a given text.

Dengue surveillance based on a computational model of spatio-temporal locality of Twitter

Twitter is a unique social media channel, in the sense that users discuss and talk about the most diverse topics, including their health conditions. In this paper we analyze how Dengue epidemic is reflected on Twitter and to what extent that information can be used for the sake of surveillance. Dengue is a mosquito-borne infectious disease that is a leading cause of illness and death in tropical and subtropical regions, including Brazil. We propose an active surveillance methodology that is based on four dimensions: volume, location, time and public perception. First we explore the public perception dimension by performing sentiment analysis. This analysis enables us to filter out content that is not relevant for the sake of Dengue surveillance. Then, we verify the high correlation between the number of cases reported by official statistics and the number of tweets posted during the same time period (i.e., R2 = 0.9578). A clustering approach was used in order to exploit the spatio-temporal dimension, and the quality of the clusters obtained becomes evident when they are compared to official data (i.e., RandIndex = 0.8914). As an application, we propose a Dengue surveillance system that shows the evolution of the dengue situation reported in tweets, which is implemented in www.observatorio.inweb.org.br/dengue/.

On word-of-mouth based discovery of the web

Traditionally, users have discovered information on the Web by browsing or searching. Recently, word-of-mouth has emerged as a popular way of discovering the Web, particularly on social networking sites like Facebook and Twitter. On these sites, users discover Web content by following URLs posted by their friends. Such word-of-mouth based content discovery has become a major driver of traffic to many Web sites today. To better understand this popular phenomenon, in this paper we present a detailed analysis of word-of-mouth exchange of URLs among Twitter users. Among our key findings, we show that Twitter yields propagation trees that are wider than they are deep. Our analysis on the geolocation of users indicates that users who are geographically close together are more likely to share the same URL.

The World of Connections and Information Flow in Twitter

Information propagation in online social networks like Twitter is unique in that word-of-mouth propagation and traditional media sources coexist. We collect a large amount of data from Twitter to compare the relative roles different types of users play in information flow. Using empirical data on the spread of news about major international headlines as well as minor topics, we investigate the relative roles of three types of information spreaders: 1) mass media sources like BBC; 2) grassroots, consisting of ordinary users; and 3) evangelists, consisting of opinion leaders, politicians, celebrities, and local businesses. Mass media sources play a vital role in reaching the majority of the audience in any major topics. Evangelists, however, introduce both major and minor topics to audiences who are further away from the core of the network and would otherwise be unreachable. Grassroots users are relatively passive in helping spread the news, although they account for the 98% of the network. Our results bring insights into what contributes to rapid information propagation at different levels of topic popularity, which we believe are useful to the designers of social search and recommendation engines.

Video interactions in online video social networks

This article characterizes video-based interactions that emerge from YouTubes video response feature, which allows users to discuss themes and to provide reviews for products or places using much richer media than text. Based on crawled data covering a representative subset of videos and users, we present a characterization from two perspectives: the video response view and the interaction network view. In addition to providing valuable statistical models for various characteristics, our study uncovers typical user behavioral patterns in video-based environments and shows evidence of opportunistic behavior.

Phi.sh/

Size, accessibility, and rate of growth of Online Social Media (OSM) has attracted cyber crimes through them. One form of cyber crime that has been increasing steadily is phishing, where the goal (for the phishers) is to steal personal information from users which can be used for fraudulent purposes. Although the research community and industry has been developing techniques to identify phishing attacks through emails and instant messaging (IM), there is very little research done, that provides a deeper understanding of phishing in online social media. Due to constraints of limited text space in social systems like Twitter, phishers have begun to use URL shortener services. In this study, we provide an overview of phishing attacks for this new scenario. One of our main conclusions is that phishers are using URL shorteners not only for reducing space but also to hide their identity. We observe that social media websites like Facebook, Habbo, Orkut are competing with e-commerce services like PayPal, eBay in terms of traffic and focus of phishers. Orkut, Habbo, and Facebook are amongst the top 5 brands targeted by phishers. We study the referrals from Twitter to understand the evolving phishing strategy. A staggering 89% of references from Twitter (users) are inorganic accounts which are sparsely connected amongst themselves, but have large number of followers and followees. We observe that most of the phishing tweets spread by extensive use of attractive words and multiple hashtags. To the best of our knowledge, this is the first study to connect the phishing landscape using blacklisted phishing URLs from PhishTank, URL statistics from bit.ly and cues from Twitter to track the impact of phishing in online social media.

oCiaL: the phishing landscape through short URLs

This paper seeks understanding the user behavior in a social network created essentially by video interactions. We present a characterization of a social network created by the video interactions among users on YouTube, a popular social networking video sharing system. Our results uncover typical user behavioral patterns as well as show evidences of anti-social behavior such as self-promotion and other types of content pollution.

Understanding video interactions in youtube

In this paper, we design and evaluate a novel who-is-who service for inferring attributes that characterize individual Twitter users. Our methodology exploits the Lists feature, which allows a user to group other users who tend to tweet on a topic that is of interest to her, and follow their collective tweets. Our key insight is that the List meta-data (names and descriptions) provides valuable semantic cues about who the users included in the Lists are, including their topics of expertise and how they are perceived by the public. Thus, we can infer a users expertise by analyzing the meta-data of crowdsourced Lists that contain the user. We show that our methodology can accurately and comprehensively infer attributes of millions of Twitter users, including a vast majority of Twitters influential users (based on ranking metrics like number of followers). Our work provides a foundation for building better search and recommendation services on Twitter.