After the peace agreement of 2016 with FARC, the killings of social leaders have emerged as an important post-conflict challenge for Colombia. We present a data analysis based on official records obtained from the Colombian General Attorney's Office spanning the time period from 2012 to 2017. The results of the analysis show a drastic increase in the officially recorded number of killings of democratically elected leaders of community organizations, in particular those belonging to Juntas de Accion Comunal [Community Action Boards]. These are important entities that have been part of the Colombian democratic apparatus since 1958, and enable communities to advocate for their needs. We also describe how the data analysis guided a journalistic investigation that was motivated by the Colombian government's denial of the systematic nature of social leaders killings.
In a working paper, @cite_9 use data from the Colombian nonprofit organization to investigate the killings of social leaders. The authors hypothesise that social leaders were increasingly killed by armed groups excluded from the peace process that wanted to consolidate their power, especially in areas where they took over FARC's illegal activities. In the dataset, @cite_9 also find that the category of social leaders targeted the most since the beginning of the ceasefire are local community council leaders.
@cite_9
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We study the unintended consequences of the recent peace process in Colombia, that ended over five decades of internal armed conflict with the FARC insurgency. Using a triple differences empirical strategy, we show that the permanent ceasefire that started in December 2014 in the context of the peace negotiations was followed by an increase in the killing of social leaders in previously FARC-dominated territories, perpetrated by other armed groups seeking control of these areas. Con- sistent with our interpretation that local social leaders are killed to thwart collective action and mobilization at the municipal level, we show that the targeting of social leaders is not explained by the behavior of the overall homicide rate and that it is exacerbated in municipalities with weaker state capacity and an inefficient local judi- ciary. Our results suggest that partial pacification processes can exacerbate violence by other existing armed groups, aimed at controlling pacified territories. Much of the data of scientific interest, particularly when independence of data is not assumed, can be represented in the form of information networks where data nodes are joined together to form edges corresponding to some kind of associations or relationships. Such information networks abound, like protein interactions in biology, web page hyperlink connections in information retrieval on the Web, cellphone call graphs in telecommunication, co-authorships in bibliometrics, crime event connections in criminology, etc. All these networks, also known as social networks, share a common property, the formation of connected groups of information nodes, called community structures. These groups are densely connected nodes with sparse connections outside the group. Finding these communities is an important task for the discovery of underlying structures in social networks, and has recently attracted much attention in data mining research. In this paper, we present Top Leaders, a new community mining approach that, simply put, regards a community as a set of followers congregating around a potential leader. Our algorithm starts by identifying promising leaders in a given network then iteratively assembles followers to their closest leaders to form communities, and subsequently finds new leaders in each group around which to gather followers again until convergence. Our intuitions are based on proven observations in social networks and the results are very promising. Experimental results on benchmark networks verify the feasibility and eectiveness of our new community mining approach. Identifying emergent leaders in organizations is a key issue in organizational behavioral research, and a new problem in social computing. This paper presents an analysis on how an emergent leader is perceived in newly formed, small groups, and then tackles the task of automatically inferring emergent leaders, using a variety of communicative nonverbal cues extracted from audio and video channels. The inference task uses rule-based and collective classification approaches with the combination of acoustic and visual features extracted from a new small group corpus specifically collected to analyze the emergent leadership phenomenon. Our results show that the emergent leader is perceived by his her peers as an active and dominant person; that visual information augments acoustic information; and that adding relational information to the nonverbal cues improves the inference of each participant's leadership rankings in the group. Abstract : The purpose of this paper is to explore the question of how modern terrorist groups manage to survive in the face of aggressive counterterrorist operations by security forces. Al Qa'ida survives to this day, despite the destruction of their Afghanistan sanctuary, the loss of countless key personnel, and continuous pressure by the United States and their allies. Why has al Qa'ida survived? Since much of the literature on terrorism focuses on how to eliminate them, this research paper focuses on why they still endure. In other words, instead of asking, "How do we kill them," this research asks, "Why don't they die?" This research employs a dynamic network analysis approach to explore the primary research question of terrorist survival. This analysis combines aspects of traditional social network analysis with a new multi-agent model that describes how terrorist groups raise agents through the organization to positions of prominence. The key to this process is the radicalization of members based on time, connectivity, and belief intensity. The testing dataset comes from the 1998 Tanzania Embassy bombing, expressed in the form of a meta-network. After four testing program iterations, the author concludes that terrorist organizational survival is based on the internal dynamics of leader selection and growth within the group as new members advance. These findings imply a number of recommendations for counterterrorist operations and intelligence activities in order to disrupt the growth and development of new leaders. Additionally, these results imply that current Joint and Army doctrine on network analysis insufficiently addresses the dynamic processes that network diagrams are intended to depict. American military counterinsurgency and counterterrorist operations can be greatly enhanced by moving from a network analysis approach based on structure to one based on dynamics. The Service of Facebook Fan Pages is one of the most popular social network platform for various organizations. Companies can interact with their own fans through the Fan Pages. The interactions include sending direct advertisement, gathering user meetings, and promoting electronic word of mouth (eWoM). For companies that use social network to gather customers' information, to identify the opinion leaders on the internet is very important, since opinion leaders are active persons and have influence on other potential customers. Based on clustering algorithm, we proposed a system that can find the opinion leaders and test our method on the Facebook Fan Pages. The data set includes 410,045 comments from 173,988 users that we gathered from October 2013 to September 2014. We also use classification methods to evaluate our system and find promising result. Abstract Social media data are increasingly being used for enhancing situational awareness and assisting disaster management. We analyzed the wildfire-related Twitter activities in terms of their attributes pertinent to space, time, content, and network, so as to gain insights into the usefulness of social media data in revealing situational awareness. Findings show that social media data can characterize the wildfire across space and over time, and thus are applicable to provide useful information on disaster situations. Second, people have strong geographical awareness during wildfire hazards and are interested in communicating situational updates related to wildfire damage (e.g., containment percentage and burned acres), wildfire response (e.g., evacuation), and appreciation to firefighters. Third, news media and local authorities are opinion leaders and play a dominant role in the wildfire retweet network. In this demo we introduce GuruMine, a pattern mining system for the discovery of leaders, i.e., influential users in social networks, and their tribes, i.e., a set of users usually influenced by the same leader over several actions. GuruMine is built upon a novel pattern mining framework for leaders discovery, that we introduced in [1]. In particular, we consider social networks where users perform actions. Actions may be as simple as tagging resources (urls) as in del.icio.us, rating songs as in Yahoo! Music, or movies as in Yahoo! Movies, or users buying gadgets such as cameras, handholds, etc. and blogging a review on the gadgets. The assumption is that actions performed by a user can be seen by their network friends. Users seeing their friends actions are sometimes tempted to perform those actions. On the basis of the propagation of such influence, in [1] we provided various notion of leaders and developed algorithms for their efficient discovery. GuruMine provides users with a friendly graphical interface for selecting the actions of interest, and the kind of leaders to mine. The set of parameters driving the pattern discovery process can be iteratively refined, and the result is updated, if possible without incurring a completely new computation. Once a set of leaders has been extracted, GuruMine can easily validate them on a set of actions unseen during the pattern mining, by analyzing the portion of network reached by the influence of the selected leaders on the unseen actions. GuruMine also offers various visualizations over the social networks: the propagation of an action, the leaders, their tribes, and the interactions between different leaders and tribes. In this demo we will show: (i) how the pattern mining process can be driven towards the discovery of a good set of leaders, (ii) the ease of use of GuruMine system, and (iii) its outstanding performances on large real-world social networks and actions databases. Twitter is presently utilized as a channel of communication and information dissemination. At present, government and non-government emergency management organizations utilize Twitter to disseminate emergency relevant information. However, these organizations have limited ability to evaluate the Twitter communication in order to discover communication patterns, key players, and messages that are being propagated through Twitter regarding the event. More importantly there is a general lack of knowledge of who are the individuals or organizations that disseminate warning information, provide confirmations of an event and associated actions, and urge others to take action. This paper presents a methodology that shows how Natural Language Processing (NLP) and Social Network Analysis (SNA) can aid in addressing these issues. The methodology, in addition to qualitative data collected during on-site interviews and publicly available information, was successfully applied to a Twitter data set collected during 2011 Japan tsunami. NLP techniques were applied to extract actionable messages. Based on the messages extracted by NLP, SNA was used to construct a network of actionable messages. While SNA discovered communities and extracted the community leaders, NLP was used to determine the behavior of the community members and the role of the community leaders. Therefore, the proposed methodology automatically finds communities, evaluates its members' behaviors, and authenticates cohesive behaviors of the community members during emergencies. Moreover, the methodology efficiently finds the leaders of the communities, while also identifying their role in communities. Abstract Over the past several decades, social network research has favored either ego-centric (e.g. employee) or bounded networks (e.g. organization) as the primary unit of analysis. This paper revitalizes a focus on the work group, which includes structural properties of both its individual members and the collection as a whole. In a study of 182 work groups in a global organization, we found that structural holes of leaders within groups as well as core-periphery and hierarchical group structures were negatively associated with performance. We show that these effects hold even after controlling for mean levels of group communication, and discuss implications for the future of network analysis in work groups and informal organizations. Understanding the processes by which task groups function is a major concern of social psychologists who study small groups. Equally important to the applied group researcher is the understanding of how group processes are altered by the external environment. Research suggests that organizations respond to stress with a centralization of authority so that decision making becomes concentrated in the higher levels of an organizational hierarchy. In this study, this hypothesis was extended to the small-group level of analysis, and the effects of stress on group status and decision making were examined. Experimental results clarify and delimit the centralization-of-authority hypothesis: Rather than centralizing authority and decision making under stress, group leaders and group members become more receptive to information provided by others. Implications for group decision making are discussed. This thesis focuses on the identification of influential users, also known as key opinion leaders, within the social network Instagram. Instagram is a very popular platform to share images with the option to categorise the images by certain tags. It is possible to collect public data from Instagram via the open API of the platform. This thesis presents a concept to create an automated crawler for this API and col- lect data into a database in order to apply algorithms from graph theory to identify opinion leaders afterwards. The sample topic for this thesis has been veganfood and all associated posts from Instagram have been crawled. After the user data has been crawled a graph has been created to do further research with common social network analysis tools. The graph contained a total set of more than 26,000 nodes. To identify opinion leaders from this graph, five di↵erent metrics have been applied, in particular PageRank, Betweenness centrality, Closeness Centrality, Degree and Eigen- vector centrality. After applying the di↵erent algorithms the results have been eval- uated and additionally an marketing expert with focus on social media analysed the results. This project was able to figured out that it is possible to find opinion leaders by using the PageRank algorithm and that those opinion leaders have a very good value of en- gagement. This indicates that they show a high interaction with other users on their posts. In conclusion the additional research options are discussed to provide a future outlook. In social network analysis, the observed data is usually some social behavior, such as the formation of groups, rather than an explicit network structure. Zhao and Weko (2017) propose a model-based approach called the hub model to infer implicit networks from grouped observations. The hub model assumes independence between groups, which sometimes is not valid in practice. In this article, we generalize the idea of the hub model into the case of grouped observations with temporal dependence. As in the hub model, we assume that the group at each time point is gathered by one leader. Unlike in the hub model, the group leaders are not sampled independently but follow a Markov chain, and other members in adjacent groups can also be correlated. An expectation-maximization (EM) algorithm is developed for this model and a polynomial-time algorithm is proposed for the E-step. The performance of the new model is evaluated under different simulation settings. We apply this model to a data set of the Kibale Chimpanzee Project. We introduce a novel frequent pattern mining approach to discover leaders and tribes in social networks. In particular, we consider social networks where users perform actions. Actions may be as simple as tagging resources (urls) as in del.icio.us, rating songs as in Yahoo! Music, or movies as in Yahoo! Movies, or users buying gadgets such as cameras, handhelds, etc. and blogging a review on the gadgets. The assumption is that actions performed by a user can be seen by their network friends. Users seeing their friends' actions are sometimes tempted to perform those actions. We are interested in the problem of studying the propagation of such "influence", and on this basis, identifying which users are leaders when it comes to setting the trend for performing various actions. We consider alternative definitions of leaders based on frequent patterns and develop algorithms for their efficient discovery. Our definitions are based on observing the way influence propagates in a time window, as the window is moved in time. Given a social graph and a table of user actions, our algorithms can discover leaders of various flavors by making one pass over the actions table. We run detailed experiments to evaluate the utility and scalability of our algorithms on real-life data. The results of our experiments confirm on the one hand, the efficiency of the proposed algorithm, and on the other hand, the effectiveness and relevance of the overall framework. To the best of our knowledge, this the first frequent pattern based approach to social network mining. The recent wave of mobilizations in the Arab world and across Western countries has generated much discussion on how digital media is connected to the diffusion of protests. We examine that connection using data from the surge of mobilizations that took place in Spain in May 2011. We study recruitment patterns in the Twitter network and find evidence of social influence and complex contagion. We identify the network position of early participants (i.e. the leaders of the recruitment process) and of the users who acted as seeds of message cascades (i.e. the spreaders of information). We find that early participants cannot be characterized by a typical topological position but spreaders tend to be more central in the network. These findings shed light on the connection between online networks, social contagion, and collective dynamics, and offer an empirical test to the recruitment mechanisms theorized in formal models of collective action. In this paper we present a multimodal analysis of emergent leadership in small groups using audio-visual features and discuss our experience in designing and collecting a data corpus for this purpose. The ELEA Audio-Visual Synchronized corpus (ELEA AVS) was collected using a light portable setup and contains recordings of small group meetings. The participants in each group performed the winter survival task and filled in questionnaires related to personality and several social concepts such as leadership and dominance. In addition, the corpus includes annotations on participants’ performance in the survival task, and also annotations of social concepts from external viewers. Based on this corpus, we present the feasibility of predicting the emergent leader in small groups using automatically extracted audio and visual features, based on speaking turns and visual attention, and we focus specifically on multimodal features that make use of the looking at participants while speaking and looking at while not speaking measures. Our findings indicate that emergent leadership is related, but not equivalent, to dominance, and while multimodal features bring a moderate degree of effectiveness in inferring the leader, much simpler features extracted from the audio channel are found to give better performance. Evolutionary community discovery is a hot research topic which clusters the dynamic or temporal network. The communities detected in dynamic network should get reasonable partition for the current data while simultaneously not deviate drastically from the previous ones. In this paper, the evolutionary community discovery algorithm based on leader nodes (EvoLeaders) is proposed to cluster the dynamic network. Compared with the static community discovery algorithm based on leader nodes (the Top Leaders algorithm), experimental results over two real-world datasets demonstrate that the EvoLeaders is more suitable for dynamic scenarios. In this note, we study a distributed coordinated tracking problem for multiple networked Euler-Lagrange systems. The objective is for a team of followers modeled by full-actuated Euler-Lagrange equations to track a dynamic leader whose vector of generalized coordinates is time varying under the constraints that the leader is a neighbor of only a subset of the followers and the followers have only local interaction. We consider two cases: i) The leader has a constant vector of generalized coordinate derivatives, and ii) The leader has a varying vector of generalized coordinate derivatives. In the first case, we propose a distributed continuous estimator and an adaptive control law to account for parametric uncertainties. In the second case, we propose a model-independent sliding mode control algorithm. Simulation results on multiple networked two-link revolute joint arms are provided to show the effectiveness of the proposed control algorithms. Centrality measures are widely used to identify leaders of covert networks. We study how a group of such leaders can avoid being detected by such measures. More concretely, we study the hardness of choosing a set of edges that can be added to the network in order to decrease the leaders' ranking according to two fundamental centrality measures, namely degree, and closeness. We prove that this problem is NP-complete for each measure. We then study how the leaders can construct a network from scratch, designed specifically for them to hide in disguise. We identify a network structure that not only guarantees to hide the leaders to a certain extent, but also allows them to spread their influence across the network. This paper provides a realization of distributed leader election without having any eventual timely links. Progress is guaranteed in the following weak setting: Eventually one process can send messages such that every message obtains f timely responses, where f is a resilience bound. A crucial facet of this property is that the f responders need not be fixed, and may change from one message to another. In particular, this means that no specific link needs to remain timely. In the (common) case where f=1, this implies that the FLP impossibility result on consensus is circumvented if one process can at any time communicate in a timely manner with one other process in the system. The protocol also bears significant practical importance to well-known coordination schemes such as Paxos, because our setting more precisely captures the conditions on the elected leader for reaching timely consensus. Additionally, an extension of our protocol provides leader stability, which guarantees against arbitrary demotion of a qualified leader and avoids performance penalties associated with leader changes in schemes such as Paxos. Device-to-device (D2D) communication as an underlay to cellular networks can bring significant benefits to users' throughput. However, as D2D user equipments (TIEs) can cause interference to cellular TIEs, the scheduling and allocation of channel resources and power to D2D communication need elaborate coordination. In this paper, we propose a joint scheduling and resource allocation scheme to improve the performance of D2D communication. We take network throughput and TIEs' fairness into account by performing interference management. Specifically, we develop a Stackelberg game framework in which we group a cellular TIE and a D2D TIE to form a leader-follower pair. The cellular user is the leader, and the D2D TIE is the follower who buys channel resources from the leader. We analyze the equilibrium of the game, and propose an algorithm for joint scheduling and resource allocation. Finally, we perform computer simulations to study the performance of the proposed algorithm.