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The popularity of online information generally experiences a rising and falling evolution. This paper considers the “burst”, “peak”, and “fade” key events together as a representative summary of popularity evolution. We propose a novel prediction task—predicting when popularity undergoes these key events. It is of great importance to know when these three key events occur, because doing so helps recommendation systems, online marketing, and containment of rumors. However, it is very challenging to solve this new prediction task due to two issues. First, popularity evolution has high variation and can follow various patterns, so how can we identify “burst”, “peak”, and “fade” in different patterns of popularity evolution? Second, these events usually occur in a very short time, so how can we accurately yet promptly predict them? In this paper we address these two issues. To handle the first one, we use a simple moving average to smooth variation, and then a universal method is presented for different patterns to identify the key events in popularity evolution. To deal with the second one, we extract different types of features that may have an impact on the key events, and then a correlation analysis is conducted in the feature selection step to remove irrelevant and redundant features. The remaining features are used to train a machine learning model. The feature selection step improves prediction accuracy, and in order to emphasize prediction promptness, we design a new evaluation metric which considers both accuracy and promptness to evaluate our prediction task. Experimental and comparative results show the superiority of our prediction solution. [ABSTRACT FROM AUTHOR]

Persuasion is at the core of norm creation, emergence of collective action, and solutions to ‘tragedy of the commons’ problems. In this paper, we show that the directionality of friendship ties affect the extent to which individuals can influence the behavior of each other. Moreover, we find that people are typically poor at perceiving the directionality of their friendship ties and that this can significantly limit their ability to engage in cooperative arrangements. This could lead to failures in establishing compatible norms, acting together, finding compromise solutions, and persuading others to act. We then suggest strategies to overcome this limitation by using two topological characteristics of the perceived friendship network. The findings of this paper have significant consequences for designing interventions that seek to harness social influence for collective action. [ABSTRACT FROM AUTHOR]

Micro-blogging systems have become one of the most important ways for information sharing. Network structure and users’ interactions such as forwarding behaviors have aroused considerable research attention, while mention, as a key feature in micro-blogging platforms which can improve the visibility of a message and direct it to a particular user beyond the underlying social structure, is seldom studied in previous works. In this paper, we empirically study the mention effect in information diffusion, using the dataset from a population-scale social media website. We find that users with high number of followers would receive much more mentions than others. We further investigate the effect of mention in information diffusion by examining the response probability with respect to the number of mentions in a message and observe a saturation at around 5 mentions. Furthermore, we find that the response probability is the highest when a reciprocal followship exists between users, and one is more likely to receive a target user’s response if they have similar social status. To illustrate these findings, we propose the response prediction task and formulate it as a binary classification problem. Extensive evaluation demonstrates the effectiveness of discovered factors. Our results have consequences for the understanding of human dynamics on the social network, and potential implications for viral marketing and public opinion monitoring. [ABSTRACT FROM AUTHOR]

Fish consumption in Mexico is considered low (around 12 kg per person per year) and non-homogeneously distributed across the country. One of the reasons for this situation is the scarcity of wholesale selling sites. In this context, the Mercado del Mar (MM), located in Guadalajara city, Jalisco, is the second biggest wholesale fish market in Mexico, with a distribution of about 500 tons per day and a variety of about 350 different species of fish. In this paper, we argue that MM has accumulated social capital, which is formed from two main resources: buyer and seller relationships, and reputation. Specifically, the MM manages a broad and intensive interaction among business actors and the already achieved reputation allows the MM to adapt to market changes. To validate our hypotheses, an empirical study was conducted in 2015 by means of interviews to fish wholesalers in the MM and a sample of their suppliers and buyers. For simplicity we have only considered fresh water fish. We have followed snow-ball sampling as the survey strategy. Results show that the MM has responded to fish market dynamics organizing a complex network of buyers and suppliers whose relationships can be explained in the form of strong and weak ties. At the same time, reputation has been the central resource to build this social capital and also gives place to market transactions. Additionally, the strategic position of Guadalajara city and the well-connected routes have facilitated fish bulking and distribution in the region. [ABSTRACT FROM AUTHOR]

This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals’ access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends. [ABSTRACT FROM AUTHOR]

Recently, increasing numbers of users have adopted microblogging services as their main information source. However, most of them find themselves drowning in the millions of posts produced by other users every day. To cope with this, identifying a set of the most influential people is paramount. Moreover, finding a set of related influential users to expand the coverage of one particular topic is required in real world scenarios. Most of the existing algorithms in this area focus on topology-related methods such as PageRank. These methods mine link structures to find the expected influential rank of users. However, because they ignore the interaction data, these methods turn out to be less effective in social networks. In reality, a variety of topics exist within the information diffusing through the network. Because they have different interests, users play different roles in the diffusion of information related to different topics. As a result, distinguishing influential leaders according to different topics is also worthy of research. In this paper, we propose a multi-topic influence diffusion model (MTID) based on traces acquired from historic information. We decompose the influential scores of users into two parts: the direct influence determined by information propagation along the link structure and indirect influence that extends beyond the restrictions of direct follower relationships. To model the network from a multi-topical viewpoint, we introduce topic pools, each of which represents a particular topic information source. Then, we extract the topic distributions from the traces of tweets, determining the influence propagation probability and content generation probability. In the network, we adopt multiple ground nodes representing topic pools to connect every user through bidirectional links. Based on this multi-topical view of the network, we further introduce the topic-dependent rank (TD-Rank) algorithm to identify the multi-topic influential users. Our algorithm not only effectively overcomes the shortages of PageRank but also effectively produces a measure of topic-related rank. Extensive experiments on a Weibo dataset show that our model is both effective and robust. [ABSTRACT FROM AUTHOR]

Abstract: The growth and popularity of online social networks has created a new world of collaboration and communication. More than a billion individuals around the world are connected and networked together to create, collaborate, and contribute their knowledge and wisdom. Despite the importance of online social networks, there is relatively little theory-driven empirical research available to address this new type of communication and interaction phenomena. In this paper, we explored the factors that drive students to use online social networks (e.g., Facebook). Specifically, we conceptualized the use of online social networks as intentional social action and we examined the relative impact of social influence, social presence, and the five key values from the uses and gratification paradigm on We-Intention to use online social networks. An empirical study of Facebook users (n =182) revealed that We-Intention to use online social networks is strongly determined by social presence. Among the five values, social related factors had the most significant impact on the intention to use. Implications for research and practice are discussed. [Copyright &y& Elsevier]

Agriculture continues to be a major contributor to water pollution, climate change and loss of biodiversity although policies to encourage farmers to work to higher sustainability standards in food and energy crop production have increased throughout the European Union. In New Member States, accession to the European Union mostly brought a substantially increased public support to foster the diffusion of certified organic farming. However, the take-up of organic farming is varied for reasons that are not yet well understood. In this paper, we analyse the diffusion of organic farming through farm populations. This involves an understanding of farmer behaviour and how it can change over time. We present a generic agent based model that builds on the Theory of Planned Behaviour as framework for understanding and modelling farmers' decision-making processes. The model is applied to high-diffusion regions in two New EU Member States, Latvia and Estonia. The values for the model's parameters are informed by survey data. The model reproduces the interdependence of social influence and economic factors. Social influence alone is shown to make little difference to the model dynamics; organic farmers remain organic, and conventional farmers remain conventional. Introducing a change to the environment (e.g. a subsidy) results in an increase in the proportion of adopters. Thus, economic factors appear to be more influential than social factors. However, only when allowing for both, the subsidy and social influence, do we reveal the whole picture and the combined adoption rate is higher than the sum of the proportion of adopters resulting from just social influence (without a subsidy) and from just a subsidy (without social influence). We also compare the effect of the subsidy with the effect of influence from organic farm advisors to develop policy recommendations. [Copyright &y& Elsevier]

Social network analysis is increasingly used in the study of policy implementation and school leadership. A key question that remains is that of instrument validity - that is, the question of whether these social network survey instruments measure what they purport to measure. In this paper, we describe our work to examine the validity of the School Staff Social Network Questionnaire (SSSNQ), an instrument designed to study instructional leadership practice. To examine the validity of the survey, we conducted two studies. The first involved administration of the SSSNQ in 22 schools and interviews with a sub-sample of school staff in six of these schools. The second study involved cognitive interviews in which interviewees were asked to 'think aloud' as they completed a revised version of the SSSNQ. Our findings indicate that the SSSNQ did identify leadership operationalized as social influence interactions. Furthermore, the SSSNQ allowed us to move away from an exclusive focus on school principals and other formally designated leaders to include non-positional leaders, and allowed us to capture informal leadership interactions that would have been missed had we focused solely on formal organizational routines. In this respect, the SSSNQ offers an important research instrument for examining school leadership. [ABSTRACT FROM AUTHOR]

Subject of this paper is the measurement of social influence in social networks. The theoretical point of departure is twofold. First, focus is on cognitive processing of perceived influence. Second, three distinct dimensions of social influence are considered: persuasion, authority and coercion. Combining these considerations with Item Response Theory methods, questionnaire-type measurement instruments are proposed. These instruments are employed in a closed network case study where applicability is checked by means of network autocorrelation models. [ABSTRACT FROM AUTHOR]

In this paper, I show that persons reach unanimous opinions even when they have different initial opinions and different social influences in social influence networks. Friedkin and Johnson introduced a model of social influence networks, and identified conditions For initially diverse opinions to converge. However, they did not examine conditions or "unanimous" convergence. Hence, I provide sufficient conditions of such unanimous consensus by focusing on three typical but conflicting social influences: the equal influence, the influence of the lowest opinion, and no influence. I show that unanimous opinions occur even when persons have antagonistic social influences such as the equal influence and the influence of the lowest opinion. I also demonstrate that the most cooperative type is the equal influence, but the most central type is the no influence. [ABSTRACT FROM AUTHOR]