Faving Reciprocity in Content Sharing Communities A comparative analysis of Flickr and Twitter

International audienceIn the Web 2.0 era, users share and discover interesting content via a network of relationships created in various social networking or content sharing sites. They can become for example contacts, followers or friends and express their appreciation of specific content uploaded by their peers by faving, retweeting or liking them depending on whether they are in Flickr, Twitter or Facebook respectively. Then they can discover additional content of interest through the lists of favorites of their contacts and so on. This faving (or favoring) functionality becomes thus a central part of content sharing communities for two purposes: (a) it helps the propagation of content amongst users and (b) it stimulates users' participation and activity. In this paper, we make a first step to understand users' faving behavior in content sharing communities in terms of reciprocity using publicly available datasets from Flickr and Twitter. Do users favor content only when they really appreciate it or they often feel the need to reciprocate when their content is appreciated by one of their contacts or even by a stranger? Do people take advantage of this process to gain popularity? What is the impact of the design, the social software, of a specific community and the type of content shared? These are some of the questions that our first results help to answer

An Approach to Model and Predict the Popularity of Online Contents with Explanatory Factors

International audienceIn this paper, we propose a methodology to predict the popularity of online contents. More precisely, rather than trying to infer the popularity of a content itself, we infer the likelihood that a content will be popular. Our approach is rooted in survival analysis where predicting the precise lifetime of an individual is very hard and almost impossible but predicting the likelihood of one's survival longer than a threshold or another individual is possible. We position ourselves in the standpoint of an external observer who has to infer the popularity of a content only using publicly observable metrics, such as the lifetime of a thread, the number of comments, and the number of views. Our goal is to infer these observable metrics, using a set of explanatory factors, such as the number of comments and the number of links in the first hours after the content publication, which are observable by the external observer. We use a Cox proportional hazard regression model that di- vides the distribution function of the observable popularity metric into two components: a) one that can be explained by the given set of explanatory factors (called risk factors) and b) a baseline distribution function that integrates all the factors not taken into account. To validate our proposed approach, we use data sets from two different online discussion forums: dpreview.com, one of the largest online discussion groups providing news and discussion forums about all kinds of digital cameras, and myspace.com, one of the representative online social networking services. On these two data sets we model two different popularity metrics, the lifetime of threads and the number of comments, and show that our approach can predict the lifetime of threads from Dpreview (Myspace) by observing a thread during the first 5∼6 days (24 hours, respectively) and the number of comments of Dpreview threads by observing a thread during first 2∼3 days

Modeling and Predicting the Popularity of Online Contents with Cox Proportional Hazard Regression Model

Special Issue on Advances in Web IntelligenceInternational audienceWe propose a general framework which can be used for modeling and predicting the popularity of online contents. The aim of our modeling is not inferring the precise popularity value of a content, but inferring the likelihood where the content will be popular. Our approach is rooted in survival analysis which deals with the survival time until an event of a failure or death. Survival analysis assumes that predicting the precise lifetime of an instance is very hard but predicting the likelihood of the lifetime of an instance is possible based on its hazard distribution. Additionally we position ourselves in the standpoint of an external observer who has to model the popularity of contents only with publicly available information. Thus, the goal of our proposed methodology is to model a certain popularity metric, such as the lifetime of a content and the number of comments which a content receives, with a set of explanatory factors, which are observable by the external observer. Among various parametric and non-parametric approaches for the survival analysis, we use the Cox proportional hazard regression model, which divides the distribution function of a certain popularity metric into two components: one which is explained by a set of explanatory factors, called risk factors, and another, a baseline survival distribution function, which integrates all the factors not taken into account. In order to validate our proposed methodology, we use two datasets crawled from two di erent discussion forums, forum.dpreview.com and forums.myspace.com, which are one of the largest discussion forum dealing various issues on digital cameras and a discussion forum provided by a representative social networks. We model two di erence popularity metrics, the lifetime of threads and the number of comments, and we show that the models can predict the lifetime of threads from Dpreview (Myspace) by observing a thread during the first 5 6 days (24 hours, respectively) and the number of comments of Dpreview threads by observing a thread during first 2 3 days

Haze Gazer: A Crisis Analysis and Visualisation Tool to Better Inform Peatland Fire and Haze Management

Peatland fires and haze events in Indonesia are disasters with national and international implications. The phenomena lead to direct damage to local assets, as well as broader economic, social and environmental losses. Despite the extensive efforts of many organizations, the situation persists. At present, Indonesian disaster management authorities manage peatland fire and haze events based on satellite data as well as static data on population density and distribution. But to better support affected populations, the Government is looking for more timely data and more information on the dynamics of the disaster, especially the situation on the ground. Pulse Lab Jakarta’s Haze Gazer enhances disaster risk management efforts by providing real-time situational information from diverse data sources, including insights on the response strategies of haze-affected communities, in order to better protect vulnerable populations and the environment

Recommending the Meanings of Newly Coined Words

AbstractIn this paper, we investigate how to recommend the meanings of newly coined words, such as newly coined named entities and Internet jargon. Our approach automatically chooses a document explaining a given newly coined word among candidate documents from multiple web references using Probabilistic Latent Semantic Analysis [1]. Briefly, it involves finding the topic of a document containing the newly coined word and computing the conditional probability of the topic given each candidate document. We validate our methodology with two real datasets from MySpace forums and Twitter by referencing three web services, Google, Urbandictionary, and Wikipedia, and we show that we properly recommend the meanings of a set of given newly coined words with 69.5% and 80.5% accuracies based on our three recommendations, respectively. Moreover, we compare our approach against three baselines where one references the result from each web service and our approach outperforms them