A data-driven analysis to question epidemic models for citation cascades on the blogosphere

Citation cascades in blog networks are often considered as traces of information spreading on this social medium. In this work, we question this point of view using both a structural and semantic analysis of five months activity of the most representative blogs of the french-speaking community.Statistical measures reveal that our dataset shares many features with those that can be found in the literature, suggesting the existence of an identical underlying process. However, a closer analysis of the post content indicates that the popular epidemic-like descriptions of cascades are misleading in this context.A basic model, taking only into account the behavior of bloggers and their restricted social network, accounts for several important statistical features of the data.These arguments support the idea that citations primary goal may not be information spreading on the blogosphere.Comment: 18 pages, 9 figures, to be published in ICWSM-13 proceeding

From coincidence to purposeful flow? properties of transcendental information cascades

In this paper, we investigate a method for constructing cascades of information co-occurrence, which is suitable to trace emergent structures in information in scenarios where rich contextual features are unavailable. Our method relies only on the temporal order of content-sharing activities, and intrinsic properties of the shared content itself. We apply this method to analyse information dissemination patterns across the active online citizen science project Planet Hunters, a part of the Zooniverse platform. Our results lend insight into both structural and informational properties of different types of identifiers that can be used and combined to construct cascades. In particular, significant differences are found in the structural properties of information cascades when hashtags as used as cascade identifiers, compared with other content features. We also explain apparent local information losses in cascades in terms of information obsolescence and cascade divergence; e.g., when a cascade branches into multiple, divergent cascades with combined capacity equal to the original

A Synthetical Approach for Blog Recommendation Mechanism: Trust, Social Relation, and Semantic Analysis

Weblog is a good paradigm of online social network which constitutes web-based regularly updated journals with reverse chronological sequences of dated entries, usually with blogrolls on the sidebars, allowing bloggers link to favorite site which they are frequently visited. In this study we propose an elaborate blog recommendation mechanism that combines trust model, social relation and semantic analysis and illustrate how it can be applied to a prestigious online blogging system – Wretch in Taiwan. By preliminary results of experimental study, we found some implications and empirically prove some theories in domain of social networking, and the example reveals that the proposed recommendation mechanism is quite feasible and promising

Survey on Link Prediction and Page Ranking In Blogs S.Geetha

This paper presents a study of the various aspects of link prediction and page ranking in blogs. Social networks have taken on a new eminence from the prospect of the analysis of social networks, which is a recent area of research which grew out of the social sciences as well as the exact sciences, especially with the computing capacity for mathematical calculations and even modelling which was previously impossible. An essential element of social media, particularly blogs, is the hyperlink graph that connects various pieces of content. Link prediction has many applications, including recommending new items in online networks (e.g., products in eBay and Amazon, and friends in Face book), monitoring and preventing criminal activities in a criminal network, predicting the next web page users will visit, and complementing missing links in automatic web data crawlers. Page Rank is the technique used by Google to determine importance of page on the web. It considers all incoming links to a page as votes for Page Rank. Our findings provide an overview of social relations and we address the problem of page ranking and link prediction in networked data, which appears in many applications such as network analysis or recommended systems. Keywords- web log, social networks analysis, readership, link prediction, Page ranking. I

Cultural Diffusion and Trends in Facebook Photographs

Online social media is a social vehicle in which people share various moments of their lives with their friends, such as playing sports, cooking dinner or just taking a selfie for fun, via visual means, that is, photographs. Our study takes a closer look at the popular visual concepts illustrating various cultural lifestyles from aggregated, de-identified photographs. We perform analysis both at macroscopic and microscopic levels, to gain novel insights about global and local visual trends as well as the dynamics of interpersonal cultural exchange and diffusion among Facebook friends. We processed images by automatically classifying the visual content by a convolutional neural network (CNN). Through various statistical tests, we find that socially tied individuals more likely post images showing similar cultural lifestyles. To further identify the main cause of the observed social correlation, we use the Shuffle test and the Preference-based Matched Estimation (PME) test to distinguish the effects of influence and homophily. The results indicate that the visual content of each user's photographs are temporally, although not necessarily causally, correlated with the photographs of their friends, which may suggest the effect of influence. Our paper demonstrates that Facebook photographs exhibit diverse cultural lifestyles and preferences and that the social interaction mediated through the visual channel in social media can be an effective mechanism for cultural diffusion.Comment: 10 pages, To appear in ICWSM 2017 (Full Paper

An Information Diffusion-Based Recommendation Framework for Micro-Blogging

Micro-blogging is increasingly evolving from a daily chatting tool into a critical platform for individuals and organizations to seek and share real-time news updates during emergencies. However, seeking and extracting useful information from micro-blogging sites poses significant challenges due to the volume of the traffic and the presence of a large body of irrelevant personal messages and spam. In this paper, we propose a novel recommendation framework to overcome this problem. By analyzing information diffusion patterns among a large set of micro-blogs that play the role of emergency news providers, our approach selects a small subset as recommended emergency news feeds for regular users. We evaluate our diffusion-based recommendation framework on Twitter during the early outbreak of H1N1 Flu. The evaluation results show that our method results in more balanced and comprehensive recommendations compared to benchmark approaches