Spread Of Messages Research Articles

A hierarchical approach for influential node ranking in complex social networks

Due to the rapid extension of social networks in recent years, a new potential has emerged for global spreading of messages and effective broadcasting of news. Identification of influential nodes within a network is now seen as a key factor for bringing this potential into action. k-shell is a measure for detection of node influence, and has already been used in some successful algorithms in this field. However, k-shell does not provide enough information about the topological positions of the nodes, and the present paper seeks to present a special hierarchical measure for dealing with this issue. Along with introduction of the above measure, it is shown that it can be used for detecting and ranking node influence. The experiments done on real-world and artificial networks demonstrate that the proposed approach can rank the influence of nodes more accurately than other approaches.

Node reactivation model to intensify influence on network targets

In this paper, we study a novel problem of influence maximization on social networks: Given a period of promotion time, a set of target users and a network in which each node can be activated by its neighbors multiple times, we aim at determining the k most influential seeds to maximize the total frequency of activations received by these target users. The promising viral marketing paradigm on social network is different from the current research in two main aspects. First, instead of maximizing the message spread over the entire social network, we focus on the target market since the business vendors almost specify the group of target users before designing its marketing strategy. Second, the status of a user is no longer a binary indicator representing either active or inactive. In the new model, the user status turns to be an integer value reflecting the amount of influences delivered to that user. In this paper, we prove the NP-hard nature of this challenging problem. We further present several strategies, including an efficient heuristic algorithm based on the simulated annealing optimization concept and a greedy algorithm as the baseline, to select the initial k seeds in pursuit of resulting quality close to the optimal one. As demonstrated in the empirical study on real data, instead of only providing the flexibility of striking a compromise between the execution efficiency and the resulting quality, our proposed heuristic algorithm can achieve high efficiency and meanwhile can obtain the target acceptance frequency even better than the greedy result in some cases, demonstrating its prominent feasibility to resolve the challenging problem efficiently.

Spreading dynamics of forget-remember mechanism.

We study extensively the forget-remember mechanism (FRM) for message spreading, originally introduced in Eur. Phys. J. B 62, 247 (2008)EPJBFY1434-602810.1140/epjb/e2008-00139-4. The freedom of specifying forget-remember functions governing the FRM can enrich the spreading dynamics to a very large extent. The master equation is derived for describing the FRM dynamics. By applying the mean field techniques, we have shown how the steady states can be reached under certain conditions, which agrees well with the Monte Carlo simulations. The distributions of forget and remember times can be explicitly given when the forget-remember functions take linear or exponential forms, which might shed some light on understanding the temporal nature of diseases like flu. For time-dependent FRM there is an epidemic threshold related to the FRM parameters. We have proven that the mean field critical transmissibility for the SIS model and the critical transmissibility for the SIR model are the lower and the the upper bounds of the critical transmissibility for the FRM model, respectively.

Implications of Changing Attitudes towards Game Meat Consumption at the Time of Ebola in Limbe, Cameroon

In this article, we examine social responses to the 2014–2016 Ebola epidemic in Cameroon, a country near to but beyond the farthest reaches of known Ebola cases. As Ebola spread in West Africa, there was mounting anxiety that the epidemic would spill over the border with Nigeria. To investigate people's responses to Ebola-fear, we interviewed a sample of fifty people living in Limbe, near the Nigerian border. We found a pattern of response that involved information-seeking, the adoption of various behavioral strategies believed to protect people from infection, and the spread of messages about Ebola prevention by word of mouth and through social media. Informed by resilience theory and recognition of the emergent nature of culture, we argue that by studying people's emotional, behavioral, and social reactions to perceived health threats, medical anthropologists can contribute to efforts to develop useful intervention in a world of enhanced infectious disease epidemic risk.

A language-based approach to modelling and analysis of Twitter interactions

More than a personal microblogging site, Twitter has been transformed by common use to an information publishing venue, which public characters, media channels and common people daily rely on for, e.g., news reporting and consumption, marketing, and social messaging. The use of Twitter in a cooperative and interactive setting calls for the precise awareness of the dynamics regulating message spreading. In this paper, we describe Twitlang, a language for modelling the interactions among Twitter accounts. The associated operational semantics allows users to precisely determine the effects of their actions on Twitter, such as post, reply-to or delete tweets. The language is implemented in the form of a Maude interpreter, Twitlanger, which takes a language term as an input and explores the computations arising from the term. By combining the strength of Twitlanger and the Maude model checker, it is possible to automatically verify communication properties of Twitter accounts. We illustrate the benefits of our executable formalisation by means of an application scenario inspired from real life. While the scenario highlights the benefits of adopting Twitter for a cooperative use in the everyday life, our analysis shows that appropriate settings are essential for a proper usage of the platform, in respect of fulfilling those communication properties expected within collaborative and interactive contexts.

Permanence and Community Structure in Complex Networks

The goal of community detection algorithms is to identify densely connected units within large networks. An implicit assumption is that all the constituent nodes belong equally to their associated community. However, some nodes are more important in the community than others. To date, efforts have been primarily made to identify communities as a whole, rather than understanding to what extent an individual node belongs to its community. Therefore, most metrics for evaluating communities, for example modularity, are global. These metrics produce a score for each community, not for each individual node. In this article, we argue that the belongingness of nodes in a community is not uniform. We quantify the degree of belongingness of a vertex within a community by a new vertex-based metric called permanence . The central idea of permanence is based on the observation that the strength of membership of a vertex to a community depends upon two factors (i) the extent of connections of the vertex within its community versus outside its community, and (ii) how tightly the vertex is connected internally. We present the formulation of permanence based on these two quantities. We demonstrate that compared to other existing metrics (such as modularity, conductance, and cut-ratio), the change in permanence is more commensurate to the level of perturbation in ground-truth communities. We discuss how permanence can help us understand and utilize the structure and evolution of communities by demonstrating that it can be used to -- (i) measure the persistence of a vertex in a community, (ii) design strategies to strengthen the community structure, (iii) explore the core-periphery structure within a community, and (iv) select suitable initiators for message spreading. We further show that permanence is an excellent metric for identifying communities. We demonstrate that the process of maximizing permanence (abbreviated as MaxPerm ) produces meaningful communities that concur with the ground-truth community structure of the networks more accurately than eight other popular community detection algorithms. Finally, we provide mathematical proofs to demonstrate the correctness of finding communities by maximizing permanence. In particular, we show that the communities obtained by this method are (i) less affected by the changes in vertex ordering, and (ii) more resilient to resolution limit, degeneracy of solutions, and asymptotic growth of values.

Racial justice activist hashtags: Counterpublics and discourse circulation

Using critical discourse analysis and network analysis, I address how racial justice activist hashtags #NotYourAsianSideKick and #SolidarityisforWhiteWomen circulate discourse across networked online publics within and outside Twitter. These hashtags showcase relationships between feminist online publics, demonstrate ways that hashtags circulate racial justice discourse, and exemplify the fluidity and intersectionality of racialized and feminist online publics. I draw on critical technocultural discourse analysis (CTDA) (Brock, 2012) as my technique in order to examine the hashtag’s discursivity. In order to analyze message spread and network relationships, I then provide a network analysis that illustrates message circulation in online feminist spheres.

GenPerm: A Unified Method for Detecting Non-Overlapping and Overlapping Communities

Detection of non-overlapping and overlapping communities are essentially the same problem. However, current algorithms focus either on finding overlapping or non-overlapping communities. We present a generalized framework that can identify both non-overlapping and overlapping communities, without any prior input about the network or its community distribution. To do so, we introduce a vertex-based metric, GenPerm, that quantifies by how much a vertex belongs to each of its constituent communities. Our community detection algorithm is based on maximizing the GenPerm over all the vertices in the network. We demonstrate, through experiments over synthetic and real-world networks, that GenPerm is more effective than other metrics in evaluating community structure. Further, we show that due to its vertex-centric property, GenPerm can be used to unfold several inferences beyond community detection, such as core-periphery analysis and message spreading. Our algorithm for maximizing GenPerm outperforms six state-of-the-art algorithms in accurately predicting the ground-truth labels. Finally, we discuss the problem of resolution limit in overlapping communities and demonstrate that maximizing GenPerm can mitigate this problem.

ТЕОРИЯ ЭПИДЕМИЙ КАК ИНСТРУМЕНТ АНАЛИЗА РАСПРОСТРАНЕНИЯ ШИРОКОВЕЩАТЕЛЬНОГО ТРАФИКА В СЕТЯХ VANET

Introduction: Emergency alert mechanism in VANET assumes that information messages are broadcast in ad hoc mode. In order to limit the flow of duplicate messages inevitable in this transmission method, specialized algorithms are provided which allow you to organize a selective retransmission mode among the total number of nodes. Purpose: We assess the possibility to apply epidemic theory as a tool of constructing an analytical model which would describe how the broadcast messages are spread. Results: We have analyzed the transient processes in the nodes associated with the spread of information messages through the network, and graduated the states of the nodes in accordance with the classification proposed by epidemic theory. Relying on some ideas about physical fundamentals and VANET message retransmission features, we obtained analytical expressions describing the change in the relative abundance for each of the selected groups of nodes. Further studies in NS-2 simulation environment allowed us to obtain statistics about the temporal proportion of each group of nodes for a model section of a road network. Comparative analysis of the analytical and modeling results using Pearson criterion showed that the obtained analytical expressions can be used to describe the transient processes in VANET nodes. Consequently, it proves that epidemic theory can be used as a tool for comparative analysis of algorithms developed for information message retransmission .

On the Privacy and Performance of Mobile Anonymous Microblogging

Microblogging is a popular form of online social networking activity. It allows users to send messages in a one-to-many publish-subscribe manner. Most current service providers are centralized and deploy a client–server model with unencrypted message content. As a consequence, all user behavior can, by default, be monitored, and censoring based on message content can easily be enforced on the server side. A distributed, peer-to-peer microblogging system consisting of mobile smartphone-equipped users that exchange group encrypted messages in an anonymous and censorship-resistant manner can alleviate privacy and censorship issues. We experimentally evaluate message spread of such systems with simulations that run on a range of synthetic and real-world mobility inputs, thus extending the previous work. We show that such systems are feasible for a range of mobility and network settings, both under normal and under adversarial conditions, e.g., under the presence of nodes which jam the network or send spam.

Emotional Feedback and the Viral Spread of Social Media Messages About Autism Spectrum Disorders.

To determine whether exchanges of emotional language between health advocacy organizations and social media users predict the spread of posts about autism spectrum disorders (ASDs). I created a Facebook application that tracked views of ASD advocacy organizations' posts between July 19, 2011, and December 18, 2012. I evaluated the association between exchanges of emotional language and viral views of posts, controlling for additional characteristics of posts, the organizations that produced them, the social media users who viewed them, and the broader social environment. Exchanges of emotional language between advocacy organizations and social media users are strongly associated with viral views of posts. Social media outreach may be more successful if organizations invite emotional dialogue instead of simply conveying information about ASDs. Yet exchanges of angry language may contribute to the viral spread of misinformation, such as the rumor that vaccines cause ASDs.

Twitter Influence on UK Vaccination and Antiviral Uptake during the 2009 H1N1 Pandemic.

Information exchange via Twitter and other forms of social media make public health communication more complex as citizens play an increasingly influential role in shaping acceptable or desired health behaviors. Taking the case of the 2009-2010 H1N1 pandemic, we explore in detail the dissemination of H1N1-related advice in the UK through Twitter to see how it was used to discourage or encourage vaccine and antiviral uptake. In three stages we conducted (1) an analysis of general content, retweeting patterns, and URL sharing, (2) a discourse analysis of the public evaluation of press releases and (3) a template analysis of conversations around vaccine and antiviral uptake, using Protection Motivation Theory (PMT) as a way of understanding how the public weighed the costs and benefits. Network analysis of retweets showed that information from official sources predominated. Analysing the spread of significant messages through Twitter showed that most content was descriptive but there was some criticism of health authorities. A detailed analysis of responses to press releases revealed some scepticism over the economic beneficiaries of vaccination, that served to undermine public trust. Finally, the conversational analysis showed the influence of peers when weighing up the risks and benefits of medication. Most tweets linked to reliable sources, however Twitter was used to discuss both individual and health authority motivations to vaccinate. The PMT framework describes the ways individuals assessed the threat of the H1N1 pandemic, weighing this against the perceived cost of taking medication. These findings offer some valuable insights for social media communication practices in future pandemics.

A method for defensing against multi-source Sybil attacks in VANET

Sybil attack can counterfeit traffic scenario by sending false messages with multiple identities, which often causes traffic jams and even leads to vehicular accidents in vehicular ad hoc network (VANET). It is very difficult to be defended and detected, especially when it is launched by some conspired attackers using their legitimate identities. In this paper, we propose an event based reputation system (EBRS), in which dynamic reputation and trusted value for each event are employed to suppress the spread of false messages. EBRS can detect Sybil attack with fabricated identities and stolen identities in the process of communication, it also defends against the conspired Sybil attack since each event has a unique reputation value and trusted value. Meanwhile, we keep the vehicle identity in privacy. Simulation results show that EBRS is able to defend and detect multi-source Sybil attacks with high performances.

Metadata-conscious anonymous messaging

Anonymous messaging platforms allow users to spread messages over a network (e.g., a social network) without revealing message authorship to other users. Popular demand for anonymous messaging is evidenced by the success of mobile apps like Whisper and Yik Yak. In such platforms, the spread of messages is typically modeled as a diffusion process. Recent advances in network analysis have revealed that such diffusion processes are vulnerable to author deanonymization by adversaries with access to metadata, such as timing information. In this work, we ask the fundamental question of how to intervene in the propagation of anonymous messages in order to make it difficult to find the source. In particular, we study the performance of a message propagation protocol called adaptive diffusion introduced in [1] . We prove that it achieves asymptotically optimal source-hiding and significantly outperforms standard diffusion. We further demonstrate empirically that adaptive diffusion hides the source effectively on real social graphs.

RISALA FI'ĀDĀB AL-MU'ALLIM BY MUHAMMAD HAYYAT AL-SINDHI RISALA FI'ĀDĀB AL-MU'ALLIM BY MUHAMMAD HAYYAT AL-SINDHI

In the life of all human being, education is an undeniable necessity. Our need was communicated to us by our Lord when He said: "Read in the name of your Lord who created (you)”) Surah al-Alaq (In the light of this need, necessity and responsibility, we can realize the importance and significance of the role of teachers who enable us fulfill this important commandment and build nations and civilizations. Teachers are the means that illuminate us in this world and the next. In order to clearly articulate the structure and constructs of the Islamic educational system and utilize it in the spread of the noble message of Islam, a number of Islamic scholars and scientists delved deep in to the study of teaching and learning. This includes the likes of Raghib al-Isfahani, Al-Ghazali and others learned scholars. The role of a teacher is far higher than just planning lessons and executing lectures and lesson plans. In Islam, the status of teachers is comparable to that of one's own parents. But what is it that gives teacher this status? What are the qualities of the teacher that gives them such a distinguished status? The current article is an effort to elaborate this. While discussing this, we have focused on the efforts of Sheikh Muhammad Hayat Sindhi who dedicated his life to teaching the noble guidance Islam on the scholarly fertile land of Sindh.

구매자와 비구매자의 구전효과

Word-of-Mouth (WOM), which is the unpaid spread of a positive marketing message from person to person, has been shown to be even more effective in influencing purchase decisions than traditional advertising channels. The effects of WOM might be different by types of persuaders (purchasers vs. non-purchasers), however, the current body of research has not given attention to the differential effects of WOM. This study focuses on whether or not potential customers are influenced by the WOM from non-purchasers as well as from purchasers, and it investigates if the impacts of WOM from non-purchasers are different with those from purchasers. Based on conjoint analyses, it concludes that potential customers are affected by the communication with non-purchasers as well as with purchasers but the impacts of non-purchasers are not greater than those of purchasers.

The Critical Periphery in the Growth of Social Protests.

Social media have provided instrumental means of communication in many recent political protests. The efficiency of online networks in disseminating timely information has been praised by many commentators; at the same time, users are often derided as “slacktivists” because of the shallow commitment involved in clicking a forwarding button. Here we consider the role of these peripheral online participants, the immense majority of users who surround the small epicenter of protests, representing layers of diminishing online activity around the committed minority. We analyze three datasets tracking protest communication in different languages and political contexts through the social media platform Twitter and employ a network decomposition technique to examine their hierarchical structure. We provide consistent evidence that peripheral participants are critical in increasing the reach of protest messages and generating online content at levels that are comparable to core participants. Although committed minorities may constitute the heart of protest movements, our results suggest that their success in maximizing the number of online citizens exposed to protest messages depends, at least in part, on activating the critical periphery. Peripheral users are less active on a per capita basis, but their power lies in their numbers: their aggregate contribution to the spread of protest messages is comparable in magnitude to that of core participants. An analysis of two other datasets unrelated to mass protests strengthens our interpretation that core-periphery dynamics are characteristically important in the context of collective action events. Theoretical models of diffusion in social networks would benefit from increased attention to the role of peripheral nodes in the propagation of information and behavior.

Impact of Repeated Exposures on Information Spreading in Social Networks.

Clustered structure of social networks provides the chances of repeated exposures to carriers with similar information. It is commonly believed that the impact of repeated exposures on the spreading of information is nontrivial. Does this effect increase the probability that an individual forwards a message in social networks? If so, to what extent does this effect influence people’s decisions on whether or not to spread information? Based on a large-scale microblogging data set, which logs the message spreading processes and users’ forwarding activities, we conduct a data-driven analysis to explore the answer to the above questions. The results show that an overwhelming majority of message samples are more probable to be forwarded under repeated exposures, compared to those under only a single exposure. For those message samples that cover various topics, we observe a relatively fixed, topic-independent multiplier of the willingness of spreading when repeated exposures occur, regardless of the differences in network structure. We believe that this finding reflects average people’s intrinsic psychological gain under repeated stimuli. Hence, it makes sense that the gain is associated with personal response behavior, rather than network structure. Moreover, we find that the gain is robust against the change of message popularity. This finding supports that there exists a relatively fixed gain brought by repeated exposures. Based on the above findings, we propose a parsimonious model to predict the saturated numbers of forwarding activities of messages. Our work could contribute to better understandings of behavioral psychology and social media analytics.

Hybrid Search Scheme for Social Networks Supported by Dynamic Weighted Distributed Label Clustering

Information searches are the most common application within social networks. Normally, the social network is modeled as a network graph, consisting of nodes (In the rest of the paper, unless otherwise specified, we will use the terms “user” and “node” interchangeably.) representing users within the network and edges representing relationships between users. Choosing the appropriate nodes to form an auxiliary structure for supporting the effective query message spreading can reduce the troublesome repeated queries. To accomplish this, a hybrid search (HS) scheme is proposed. If the query message is received by a node belonging the auxiliary structure constructed by dynamic weighted distributed label clustering (DW-DLC), it would be flooded to all neighbors of the visited node; otherwise, it would be forwarded to one neighbor of the visited node. The DW-DLC based auxiliary structure can accelerate the process of obtaining required information within the network. The simulation results show that the HS+DW-DLC scheme can reduce the average searching delay time, even in a required-information-scarce social network. In addition, the proposed scheme can generate a relatively low amount of repeated messages to lower repeatedly asking social network users.

Competing for Attention in Social Media under Information Overload Conditions.

Modern social media are becoming overloaded with information because of the rapidly-expanding number of information feeds. We analyze the user-generated content in Sina Weibo, and find evidence that the spread of popular messages often follow a mechanism that differs from the spread of disease, in contrast to common belief. In this mechanism, an individual with more friends needs more repeated exposures to spread further the information. Moreover, our data suggest that for certain messages the chance of an individual to share the message is proportional to the fraction of its neighbours who shared it with him/her, which is a result of competition for attention. We model this process using a fractional susceptible infected recovered (FSIR) model, where the infection probability of a node is proportional to its fraction of infected neighbors. Our findings have dramatic implications for information contagion. For example, using the FSIR model we find that real-world social networks have a finite epidemic threshold in contrast to the zero threshold in disease epidemic models. This means that when individuals are overloaded with excess information feeds, the information either reaches out the population if it is above the critical epidemic threshold, or it would never be well received.