Comparing information diffusion mechanisms by matching on cascade size

2021 ◽  
Vol 118 (46) ◽  
pp. e2100786118
Author(s):  
Jonas L. Juul ◽  
Johan Ugander
Keyword(s):  
Structural Properties ◽  
Information Diffusion ◽  
Spreading Process ◽  
Information Spread ◽  
Structural Differences ◽  
Classical Models ◽  
Diffusion Mechanisms ◽  
Speed Of Propagation ◽  
Types Of Information ◽  
False News

Do some types of information spread faster, broader, or further than others? To understand how information diffusions differ, scholars compare structural properties of the paths taken by content as it spreads through a network, studying so-called cascades. Commonly studied cascade properties include the reach, depth, breadth, and speed of propagation. Drawing conclusions from statistical differences in these properties can be challenging, as many properties are dependent. In this work, we demonstrate the essentiality of controlling for cascade sizes when studying structural differences between collections of cascades. We first revisit two datasets from notable recent studies of online diffusion that reported content-specific differences in cascade topology: an exhaustive corpus of Twitter cascades for verified true- or false-news content by Vosoughi et al. [S. Vosoughi, D. Roy, S. Aral. Science 359, 1146–1151 (2018)] and a comparison of Twitter cascades of videos, pictures, news, and petitions by Goel et al. [S. Goel, A. Anderson, J. Hofman, D. J. Watts. Manage. Sci. 62, 180–196 (2016)]. Using methods that control for joint cascade statistics, we find that for false- and true-news cascades, the reported structural differences can almost entirely be explained by false-news cascades being larger. For videos, images, news, and petitions, structural differences persist when controlling for size. Studying classical models of diffusion, we then give conditions under which differences in structural properties under different models do or do not reduce to differences in size. Our findings are consistent with the mechanisms underlying true- and false-news diffusion being quite similar, differing primarily in the basic infectiousness of their spreading process.

scholarly journals A Survey of Information Cascade Analysis

2021 ◽  
Vol 54 (2) ◽  
pp. 1-36
Author(s):  
Fan Zhou ◽  
Xovee Xu ◽  
Goce Trajcevski ◽  
Kunpeng Zhang
Keyword(s):  
Information Diffusion ◽  
Research Work ◽  
Viral Marketing ◽  
Graph Representation ◽  
Digital Information ◽  
Information Cascades ◽  
Challenges And Opportunities ◽  
Scientific Papers ◽  
Popularity Prediction ◽  
Types Of Information

The deluge of digital information in our daily life—from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising—offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades. Abundant research efforts, both academic and industrial, have aimed to reach a better understanding of the mechanisms driving the spread of information and quantifying the outcome of information diffusion. This article presents a comprehensive review and categorization of information popularity prediction methods, from feature engineering and stochastic processes , through graph representation , to deep learning-based approaches . Specifically, we first formally define different types of information cascades and summarize the perspectives of existing studies. We then present a taxonomy that categorizes existing works into the aforementioned three main groups as well as the main subclasses in each group, and we systematically review cutting-edge research work. Finally, we summarize the pros and cons of existing research efforts and outline the open challenges and opportunities in this field.

Structure Based Grading of Kinematic Chains

2014 ◽  
Vol 575 ◽  
pp. 501-506 ◽  
Author(s):  
Shubhashis Sanyal ◽  
G.S. Bedi
Keyword(s):  
Structural Properties ◽  
Structural Property ◽  
Kinematic Chain ◽  
Degree Of Freedom ◽  
Kinematic Chains ◽  
Structural Differences ◽  
The Individual ◽  
Independent Loops

Kinematic chains differ due to the structural differences between them. The location of links, joints and loops differ in each kinematic chain to make it unique. Two similar kinematic chains will produce similar motion properties and hence are avoided. The performance of these kinematic chains also depends on the individual topology, i.e. the placement of its entities. In the present work an attempt has been made to compare a family of kinematic chains based on its structural properties. The method is based on identifying the chains structural property by using its JOINT LOOP connectivity table. Nomenclature J - Number of joints, F - Degree of freedom of the chain, N - Number of links, L - Number of basic loops (independent loops plus one peripheral loop).

On the Event Structural Properties of the English Get-Passive

2020 ◽  
pp. 1-74
Author(s):  
Alison Biggs ◽  
David Embick
Keyword(s):  
Division Of Labor ◽  
Structural Properties ◽  
Argument Structure ◽  
Event Structure ◽  
Test Case ◽  
Event Structures ◽  
Structural Differences ◽  
Responsible Party

An important ongoing discussion in theories of argument structure concerns the explanatory division of labor between thematic properties and event structure. In this context, the English get-passive provides an interesting test case. Much previous work has analyzed get-passives as differing thematically from be-passives. Yet many get-passive properties remain poorly understood. We present an analysis of the get-passive centered on the proposal that it contains additional event structure (realized as get) relative to its be counterpart. We employ by-adjuncts to identify the event structures in passive types, and demonstrate that the behavior of this and other diagnostics support the conclusion that get- and be-passives differ systematically in ways that accord with our analysis. Further discussion considers the prominent proposal from previous studies that get-passives differ thematically from be-passives in (sometimes) assigning an Agent role to their surface subjects. We show that there is no evidence for such an analysis. Instead, intuitions about the interpretation of the get-passive surface subject arise from how an event’s Responsible Party is identified: contrasts between get and be on this dimension are a consequence of event structural differences between the two. The overall result is a unified analysis of the get-passive, one that has implications for the role of event structure in understanding the syntax and interpretation of arguments.

Mechanism analysis of competitive information asynchronous dissemination on social networks

2019 ◽  
Vol 30 (11) ◽  
pp. 1950094 ◽  
Author(s):  
Jianye Yu ◽  
Junjie Lv ◽  
Yuanzhuo Wang ◽  
Jingyuan Li
Keyword(s):  
Social Networks ◽  
Information Diffusion ◽  
Information Dissemination ◽  
Evolutionary Game ◽  
Real Data ◽  
Game Model ◽  
Mechanism Analysis ◽  
Sina Weibo ◽  
Human Behaviors ◽  
Information Spread

Information dissemination groups, especially those disseminating the same kind of information such as advertising, product promotion, etc., compete with each other when their information spread on social networks. Most of the existing methods analyze the dissemination mechanism mainly upon the information itself without considering human characteristics, e.g. relation networks, cooperation/defection, etc. In this paper, we introduce a framework of social evolutionary game (SEG) to investigate the influence of human behaviors in competitive information dissemination. Coordination game is applied to represent human behaviors in the competition of asynchronous information diffusion. We perform a series of simulations through a specific game model to analyze the mechanism and factors of information diffusion, and show that when the benefits of competitive information is around 1.2 times of the original one, it can compensate the loss of reputation caused by the change of strategy. Furthermore, through experiments on a dataset of two films on Sina Weibo, we described the mechanism of competition evolution over real data of social network, and validated the effectiveness of our model.

scholarly journals Predictive Analytical Model for Microblogging Data Using Asset Bubble Modelling

2020 ◽  
Vol 14 (2) ◽  
pp. 108-118
Author(s):  
Srinidhi Hiriyannaiah ◽  
Siddesh G.M. ◽  
Srinivasa K.G.
Keyword(s):  
Social Media ◽  
Information Diffusion ◽  
Predictive Analytics ◽  
Heterogeneous Systems ◽  
Asset Bubble ◽  
Proposed Model ◽  
Different Types ◽  
Social Media Platforms ◽  
Types Of Information ◽  
Better Than

In recent days, social media plays a significant role in the ecosystem of the big data world and its different types of information. There is an emerging need for collection, monitoring, analyzing, and visualizing the different information from various social media platforms in different domains like businesses, public administration, and others. Social media acts as the representative with numerous microblogs for analytics. Predictive analytics of such microblogs provides insights into various aspects of the real-world entities. In this article, a predictive model is proposed using the tweets generated on Twitter social media. The proposed model calculates the potential of a topic in the tweets for the prediction purposes. The experiments were conducted on tweets of the regional election in India and the results are better than the existing systems. In the future, the model can be extended for analysis of information diffusion in heterogeneous systems.

A community-based approach to identify the most influential nodes in social networks

2016 ◽  
Vol 43 (2) ◽  
pp. 204-220 ◽  
Author(s):  
Maryam Hosseini-Pozveh ◽  
Kamran Zamanifar ◽  
Ahmad Reza Naghsh-Nilchi
Keyword(s):  
Social Networks ◽  
Information Diffusion ◽  
Closeness Centrality ◽  
Centrality Measures ◽  
Community Based ◽  
Spreading Process ◽  
Centrality Measure ◽  
Influence Spread ◽  
The Social ◽  
Influential Nodes

One of the important issues concerning the spreading process in social networks is the influence maximization. This is the problem of identifying the set of the most influential nodes in order to begin the spreading process based on an information diffusion model in the social networks. In this study, two new methods considering the community structure of the social networks and influence-based closeness centrality measure of the nodes are presented to maximize the spread of influence on the multiplication threshold, minimum threshold and linear threshold information diffusion models. The main objective of this study is to improve the efficiency with respect to the run time while maintaining the accuracy of the final influence spread. Efficiency improvement is obtained by reducing the number of candidate nodes subject to evaluation in order to find the most influential. Experiments consist of two parts: first, the effectiveness of the proposed influence-based closeness centrality measure is established by comparing it with available centrality measures; second, the evaluations are conducted to compare the two proposed community-based methods with well-known benchmarks in the literature on the real datasets, leading to the results demonstrate the efficiency and effectiveness of these methods in maximizing the influence spread in social networks.

scholarly journals MODELING INFORMATION SPREADING ON COMPLEX NETWORKS

2017 ◽  
Vol 33 (1-2) ◽  
Author(s):  
Daniel Trpevski ◽  
Kire Stamenov ◽  
Ljupčo Kocarev
Keyword(s):  
Complex Networks ◽  
Stable State ◽  
Natural Generalization ◽  
Time Step ◽  
Spreading Process ◽  
Sis Model ◽  
Simultaneous Existence ◽  
Information Type ◽  
Types Of Information ◽  
High Degree

A b s t r a c t: In this article we propose a model for the spread of two types of information in networks. The model is a natural generalization of the epidemic susceptible-infective-susceptible(SIS) model. The two information types have different attractiveness, which affects the nodes' decision on which information type to adopt when both arrive at a node in the same time step. At difference with results from other authors, the model shows simultaneous existence of the two information types in the stable state. We give approximations for the average number of nodes informed with each information type at the end of the spreading process when nodes have high degree.

Mining Information Spreading Based on Users' Retweet Behavior in Twitter

2013 ◽  
Vol 380-384 ◽  
pp. 2866-2870 ◽  
Author(s):  
Rong Ze Xia ◽  
Yan Jia ◽  
Wang Qun Lin ◽  
Hu Li
Keyword(s):  
Social Networks ◽  
Power Law ◽  
Power Law Distribution ◽  
Spreading Process ◽  
Spreading Model ◽  
The World ◽  
Information Spread ◽  
Information Spreading

Twitter is one of the largest social networks in the world. People could share contents on it. When we interact with each other, the information spreads. And its users retweet behavior that makes information spread so fast. So there comes an important question: Whats about users retweet behavior? Could we simulate information spreading in twitter by retweeting behavior? We crawl twitter and mine information spreading based on users retweet behavior in it. Through our dateset, we verify the power-law distribution of the retweet-width and retweet-depth. At the same time, we study the correlation between retweet-width and retweet-depth. Finally, we propose an information spreading model to simulate the information spreading process in twitter and get a good result.

scholarly journals Modeling Random Forwarding Actions for Information Diffusion over Mobile Social Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Ling Li ◽  
Min Liu ◽  
Weiming Shen ◽  
Guoqing Cheng
Keyword(s):  
Social Networks ◽  
Generating Function ◽  
Information Diffusion ◽  
Function Method ◽  
Mobile Social Networks ◽  
Interference Theory ◽  
Proposed Model ◽  
Novel Method ◽  
Types Of Information

Modeling information diffusion over social networks has attracted a lot of attention from both academia and industry. Based on universal generating function method and discrete stress-strength interference theory, a novel method is proposed to model the users’ random forwarding actions, and the most susceptible users are extracted. The effect of a user on information diffusion is quantified as node susceptibility (NS), and NS is defined as the probability that quantity of information (message) the user forwards is larger than that he receives. The model can address three questions: which users are most susceptible, which types of information they are most susceptible to, and when they are most susceptible. The solutions of these questions are very helpful for the practitioners. A case study is used to illustrate the feasibility and practicality of the proposed model.

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