Predicting information diffusion on social networks with partial knowledge (original) (raw)

Predicting Information Diffusion in Social Networks Using Content and User’s Profiles

2013

Models of information diffusion and propagation over large social media usually rely on a Close World Assumption: information can only propagate onto the network relational structure, it cannot come from external sources, the network structure is supposed fully known by the model. These assumptions are nonrealistic for many propagation processes extracted from Social Websites. We address the problem of predicting information propagation when the network diffusion structure is unknown and without making any closed world assumption. Instead of modeling a diffusion process, we propose to directly predict the final propagation state of the information over a whole user set. We describe a general model, able to learn predicting which users are the most likely to be contaminated by the information knowing an initial state of the network. Different instances are proposed and evaluated on artificial datasets.

Content diffusion prediction in social networks

The 5th Conference on Information and Knowledge Technology, 2013

Social networks are valuable resources for analyzing users' natural behavior. User profile information, social links and interchanging opinions among users in these networks can be used by social analyzers to discover mental and behavioral patterns of users in social networks. In this paper, news agencies are used as the social media to detect effective factors of diffusing contents in public. We believe that the volume of comments on content show how well the content has spread and attracted attentions. As a result, we extract features of contents to predict volume of comments. To achieve this goal, content of the news articles and its publication time are considered as two critical factors. A novel method for prediction of content diffusion is proposed and its accuracy is evaluated. The promising results of our experiments indicate that these factors can gain accuracy of at least 70%.

Information diffusion in online social networks

Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium - SIGMOD'13 PhD Symposium, 2013

Online social networks play a major role in the spread of information at very large scale and it becomes essential to provide means to analyze this phenomenon. Analyzing information diffusion proves to be a challenging task since the raw data produced by users of these networks are a flood of ideas, recommendations, opinions, etc. The aim of this PhD work is to help in the understanding of this phenomenon. So far, our contributions are the following: (i) a survey of developments in the field; (ii) T-BaSIC, a graph-based model for information diffusion prediction; (iii) SONDY, an open source platform that helps understanding social network users' interests and activity by providing emerging topics and events detection as well as network analysis functionalities.

A Prediction Model for Information Diffusion in Online Social Network

International Journal of Computer Applications (0975 –8887), 2021

The emergence of online social networks has brought many new platforms, e.g., Facebook, Flickr, YouTube, Sina Micro-blog, LinkedIn, and Twitter. These platforms are imperative constituents within the diffusion of information at an expansive scale, and Twitter is among the foremost utilized microblogging and online social organizing administrations. In Twitter, a title, phrase, or point tweeted at a greater rate than others are called a "trending topic" or "trend," and it becomes imperative to make available ways to evaluate this phenomenon. Assessing information diffusion appears to be an unsolvable perplex as these "trending topics" constitute a flood of views, thoughts, recommendations, considerations, proposals, etc., produced by users of these social networks. This paper thoroughly examined Twitter's trending topics in September 2019. We accessed Twitter's trends API for the month's trending topics and concocted six criteria to assess the dataset. These six criteria are location, lexical analysis, trending time, tweet volume, promo/giveaway, and social media influencer. Based on the criteria earlier mentioned, a prediction model was developed based on these criteria. Their results were used to predict how a piece of information would diffuse on the Twitter platform.

Revisiting Informational Cascades in Online Social Networks

Revisiting Information Cascades in Online Social Networks, 2024

It is widely believed that a user’s activity pattern in Online Social Networks (OSNs) is strongly influenced by their friends or the users they follow. Building on this intuition, numerous models have been proposed over the years to predict information propagation in OSNs. Many of these models drew inspiration from the process of infectious spread within a population. While this approach is definitely plausible, it relies on knowledge of users’ social connections, which can be challenging to obtain due to privacy concerns. Moreover, while a significant body of work has focused on predicting macro-level features, such as the total cascade size, relatively little attention has been given to the prediction of micro-level features, such as the activity of an individual user. In this study we aim to address this gap by proposing a method to predict the activity of individual users in an OSN, relying solely on their interactions rather than prior knowledge of their social network. We evaluated our results on four large datasets, each comprising over 14 million tweets, recorded on X social network across four different topics over several month. Our method achieved a mean 𝐹1 score of 0.86, with a best result of 0.983.

Intelligent Prediction of Private Information Diffusion in Social Networks

Electronics

In the information age, leaked private information may cause significant physical and mental harm to the relevant parties, leading to a negative social impact. In order to effectively evaluate the impact of such information leakage in today’s social networks, it is necessary to accurately predict the scope and depth of private information diffusion. By doing so, it would be feasible to prevent and control the improper spread and diffusion of private information. In this paper, we propose an intelligent prediction method for private information diffusion in social networks based on comprehensive data analysis. We choose Sina Weibo, one of the most prominent social networks in China, to study. Firstly, a prediction model of message forwarding behavior is established by analyzing the characteristic factors that influence the forwarding behavior of the micro-blog users. Then the influence of users is calculated based on the interaction time and topological structure of users relationshi...

Learning Information Spread in Content Networks

Information propagation is a hard task where the goal is to predict users behavior. We introduce an extension of a model which make use of a kernel to modelize diffusion in a latent space. This extension introduce a threhsold to differentiate if users are contaminated or not. Preliminary work. Under review by the International Conference on Machine Learning (ICML). Do not distribute.

A Survey on Information Diffusion in Online Social Networks: Models and Methods

Information

By now, personal life has been invaded by online social networks (OSNs) everywhere. They intend to move more and more offline lives to online social networks. Therefore, online social networks can reflect the structure of offline human society. A piece of information can be exchanged or diffused between individuals in social networks. From this diffusion process, lots of latent information can be mined. It can be used for market predicting, rumor controlling, and opinion monitoring among other things. However, the research of these applications depends on the diffusion models and methods. For this reason, we survey various information diffusion models from recent decades. From a research process view, we divide the diffusion models into two categories—explanatory models and predictive models—in which the former includes epidemics and influence models and the latter includes independent cascade, linear threshold, and game theory models. The purpose of this paper is to investigate the...

Information Diffusion in Online Social Networks: A Survey

Online social networks play a major role in the spread of information at very large scale. A lot of effort have been made in order to understand this phenomenon, ranging from popular topic detection to information diffusion modeling, including influential spreaders identification. In this article, we present a survey of representative methods dealing with these issues and propose a taxonomy that summarizes the state-of-the-art. The objective is to provide a comprehensive analysis and guide of existing efforts around information diffusion in social networks. This survey is intended to help researchers in quickly understanding existing works and possible improvements to bring.

Learning social network embeddings for predicting information diffusion

Proceedings of the 7th ACM international conference on Web search and data mining - WSDM '14, 2014

Analyzing and modeling the temporal diffusion of information on social media has mainly been treated as a diffusion process on known graphs or proximity structures. The underlying phenomenon results however from the interactions of several actors and media and is more complex than what these models can account for and cannot be explained using such limiting assumptions. We introduce here a new approach to this problem whose goal is to learn a mapping of the observed temporal dynamic onto a continuous space. Nodes participating to diffusion cascades are projected in a latent representation space in such a way that information diffusion can be modeled efficiently using a heat diffusion process. This amounts to learning a diffusion kernel for which the proximity of nodes in the projection space reflects the proximity of their infection time in cascades. The proposed approach possesses several unique characteristics compared to existing ones. Since its parameters are directly learned from cascade samples without requiring any additional information, it does not rely on any pre-existing diffusion structure. Because the solution to the diffusion equation can be expressed in a closed form in the projection space, the inference time for predicting the diffusion of a new piece of information is greatly reduced compared to discrete models. Experiments and comparisons with baselines and alternative models have been performed on both synthetic networks and real datasets. They show the effectiveness of the proposed method both in terms of prediction quality and of inference speed.