Predicting emotions in online social networks: challenges and opportunities

Online social networking has become a popular means of information exchange and social interactions. Users of these platforms generate massive amounts of data about their relationships, behaviors, interests, opinions, locations visited, items purchased, and subjective experiences of various aspects of life. Moreover, these platforms enable people from wide-ranging social and cultural backgrounds to synergize and interact. One interesting area of research is the emotional dimensions contained in this user-generated content, specifically, emotion detection and prediction, which involve the extraction and analysis of emotions in social network data. This study aimed to provide a comprehensive overview and better understanding of the current state of research regarding emotion detection in online social networks by performing a systematic literature review (SLR). SLRs help identify the gaps, challenges, and opportunities in a field of study through a careful examination of current research to understand the methods and results, ultimately highlighting methodological concerns that can be used to improve future work in the field. Hence, we collected and analyzed studies that focused on emotion in social network posts and discussed various topics published in digital databases between 2010 and December 2020. Over 239 articles were initially included in the collection, and after the selection process and application of our quality criteria, 104 articles were examined, and the results showed a robust extant body of literature on the text-based emotion analysis model, while the image-based requires more attention as well as the multiple modality emotion analysis.

     

Representing and applying knowledge for argumentation in a social context

The concept of argumentation in AI is based almost exclusively on the use of formal, abstract representations. Despite their appealing computational properties, these abstractions become increasingly divorced from their real world counterparts, and, crucially, lose the ability to express the rich gamut of natural argument forms required for creating effective text. In this paper, the demands that socially situated argumentation places on knowledge representation are explored, and the various problems with existing formalisations are discussed. Insights from argumentation theory and social psychology are then adduced as key contributions to a notion of social context which is both computationally tractable and suitably expressive for handling the complexities of argumentation found in natural language.     

社交网络节点中心性测度

研究节点影响力以及扩大节点影响力的范围在社交网络传播中具有重大意义。为了综合分析节点自身影响力与其潜在影响力,提出了PPI（Personal-Potential Influence,PPI）算法,用介数中心性值,紧密中心性值及k-shell值加权来评估节点自身影响力,再通过节点间的相互影响来评估其潜在影响力。实验结果表明PPI算法在评估节点影响力上有较好的准确性。     

无线社会网路由缓存管理和服务质量的研究

提出一种针对缓存容量受限的容迟移动网络的多复制路由和缓存管理的方法。该方法能充分利用有限的网络资源,实现更高的传送成功率,降低平均发送延迟,同时实现不同重要性报文的服务质量分级。     

A temporal modal defeasible logic for formalizing social commitments in dialogue and argumentation models

In this paper, we extend a temporal defeasible logic with a modal operator Committed to formalize commitments that agents undertake as a consequence of communicative actions (speech acts) during dialogues. We represent commitments as modal sentences. The defeasible dual of the modal operator Committed is a modal operator called Exempted. The logical setting makes the social-commitment based semantics of speech acts verifiable and practical; it is possible to detect if, and when, a commitment is violated and/or complied with. One of the main advantages of the proposed system is that it allows for capturing the nonmonotonic behavior of the commitments induced by the relevant speech acts.     

移动社会网络中社会性路由算法研究

在无法部署Sink的无线传感器网络中, 数据采集者(即:能够收集数据的人或移动设备)在网络的任意位置收集数据, 即泛在数据收集。网络区域中的节点数量庞大, 能量有限, 如何能有效地采集到全部节点的数据是一个难点。提出一个网络生命周期最大化的泛在数据收集协议MULAC。MULAC以用户所在当前位置为圆心, 半径为r的区域内选择一个节点v。以v为根构造一棵最大化生命周期树T。网络中的节点可以通过T传送数据给v, 数据采集者可以通过v接收到网络中的全部数据。当数据采集者移动到其他位置, T将根据用户新的位置改变根节点, 并且以最小的能量耗费调整树结构, 从而延长全网的寿命。在收集数据过程中保证无线传感器网络生命周期最大化是一个NP完全问题, MULAC能够近似最优的解决此问题。仿真实验和理论分析表明, MULAC能有效延长网络生命周期。     

在线社会网络的k-shell结构研究

分析研究了Twitter与You Tube两个在线社会网络的结构。用k-shell(k-壳)分解法对网络分解,并对比分析了它们的入(出)度、入(出)k-shell、以及度与k-shell之间的关系,发现它们之间有较大的差异。You Tube的入(出)度、入(出)k-shell分布均服从幂律分布,而Twitter的分布服从漂移幂律分布、指数截断的幂律分布,但它们的度与k-shell关系基本相同,都未表现出较强的相关性。此外,根据度相关系数的定义还提出k-shell相关性的定义及其计算方法,并用来刻画网络k-shell之间的同(异)配性。     

Sensor networks for emergency response: challenges and opportunities

Sensor networks, a new class of devices has the potential to revolutionize the capture, processing, and communication of critical data for use by first responders. CodeBlue integrates sensor nodes and other wireless devices into a disaster response setting and provides facilities for ad hoc network formation, resource naming and discovery, security, and in-network aggregation of sensor-produced data. We designed CodeBlue for rapidly changing, critical care environments. To test it, we developed two wireless vital sign monitors and a PDA-based triage application for first responders. Additionally, we developed MoteTrack, a robust radio frequency (RF)-based localization system, which lets rescuers determine their location within a building and track patients. Although much of our work on CodeBlue is preliminary, our initial experience with medical care sensor networks raised many exciting opportunities and challenges.     

Group emotions: the social and cognitive functions of emotions in argumentation

The learning sciences of today recognize the tri-dimensional nature of learning as involving cognitive, social and emotional phenomena. However, many computer-supported argumentation systems still fail in addressing the socio-emotional aspects of group reasoning, perhaps due to a lack of an integrated theoretical vision of how these three dimensions interrelate to each other. This paper presents a multi-dimensional and multi-level model of the role of emotions in argumentation, inspired from a multidisciplinary literature review and extensive previous empirical work on an international corpus of face-to-face student debates. At the crossroads of argumentation studies and research on collaborative learning, employing a linguistic perspective, we specify the social and cognitive functions of emotions in argumentation. The cognitive function of emotions refers to the cognitive and discursive process of schematization (Grize, 1996, 1997). The social function of emotions refers to recognition-oriented behaviors that correspond to engagement into specific types of group talk (e. g. Mercer in Learning and Instruction 6(4), 359–377, 1996). An in depth presentation of two case studies then enables us to refine the relation between social and cognitive functions of emotions. A first case gives arguments for associating low-intensity emotional framing, on the cognitive side, with cumulative talk, on the social side. A second case shows a correlation between high-intensity emotional framing, and disputational talk. We then propose a hypothetical generalization from these two cases, adding an element to the initial model. In conclusion, we discuss how better understanding the relations between cognition and social and emotional phenomena can inform pedagogical design for CSCL.     

An application of formal argumentation: Fusing Bayesian networks in multi-agent systems

We consider a multi-agent system where each agent is equipped with a Bayesian network, and present an open framework for the agents to agree on a possible consensus network. The framework builds on formal argumentation, and unlike previous solutions on graphical consensus belief, it is sufficiently general to allow for a wide range of possible agreements to be identified.     

A clique-superposition model for social networks

How does a social network evolve? Sociologists have studied this question for many years.According to some famous sociologists,social links are driven by social intersections.Actors who affiliate with the shared intersections tend to become interpersonally linked and form a cluster.In the social network,an actor cluster could be a clique or a group of several smaller-sized cliques.Thus we can conclude that a social network is composed of superposed cliques of different sizes.However,sociologists did not verify the theory in large scale data due to lack of computing ability.Motivated by this challenge,incorporated with the theory,we utilize data mining technologies to study the evolution patterns of large scale social networks in real world.Then,we propose a novel Clique-superposition generative model,which generates undirected weighted networks.By extensive experiments,we demonstrate that our model can generate networks with static and time evolving patterns observed not only in earlier literature but also in our work.     

基于信息混淆的社会网络隐私保护机制

提出了一种基于信息混淆的社会网络隐私保护机制,其原理在于对整个社会网络里的隐私信息进行混淆,而非加密,使得需要保护的隐私信息以环形结构在社会网络里扩散开来。该机制以非集中化的方式工作,由用户之间的相互协作来保护用户的隐私信息。以"人人网"为平台,利用Firefox的扩展开发功能实现了该隐私保护的核心机制,证明了其可行性与可用性。该机制能够保证多方面的利益:要求隐私保护的主体用户、广告商、经过授权的用户及第三方应用。     

社交网络中模块关系树的相似性算法的研究

提出一种基于模块关系树的分析方法,考虑每个实体与用户之间的兴趣、住址和共同好友等相关因素,制定不同的关系树,然后根据路径长度计算各因素的相关度值,最后综合每个实体模块,从而筛选出关系最密切的实体。实验结果证明,该算法能过滤掉大量无关信息,有效找出最相关的实体,提高了搜索结果的准确率。     

Leveraging social media networks for classification

Social media has reshaped the way in which people interact with each other. The rapid development of participatory web and social networking sites like YouTube, Twitter, and Facebook, also brings about many data mining opportunities and novel challenges. In particular, we focus on classification tasks with user interaction information in a social network. Networks in social media are heterogeneous, consisting of various relations. Since the relation-type information may not be available in social media, most existing approaches treat these inhomogeneous connections homogeneously, leading to an unsatisfactory classification performance. In order to handle the network heterogeneity, we propose the concept of social dimension to represent actors?? latent affiliations, and develop a classification framework based on that. The proposed framework, SocioDim, first extracts social dimensions based on the network structure to accurately capture prominent interaction patterns between actors, then learns a discriminative classifier to select relevant social dimensions. SocioDim, by differentiating different types of network connections, outperforms existing representative methods of classification in social media, and offers a simple yet effective approach to integrating two types of seemingly orthogonal information: the network of actors and their attributes.     

Community detection for emerging social networks

Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the “New Network Community Detection” problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively.     

Searching for people to follow in social networks

With the development of social networks, more and more users have a great need to search for people to follow (SPTF) to receive their tweets. According to our experiments, approximately 50% of social networks’ lost users leave due to a lack of people to follow. In this paper, we define the problem of SPTF and propose an approach to give users tags and then deliver a ranked list of valuable accounts for them to follow. In the proposed approach, we first seek accounts related to keywords via expanding and predicting tags for users. Second, we propose two algorithms to rank relevant accounts: the first mines the forwarded relationship, and the second incorporates the following relationship into PageRank. Accordingly, we have built a search system¹ that to date, has received more than 1.7 million queries from 0.2 million users. To evaluate the proposed approach, we created a crowd-sourcing organization and crawled 0.25 billion profiles, 15 billion messages and 20 billion links representing following relationships on Sina Microblog. The empirical study validates the effectiveness of our algorithms for expanding and predicting tags compared to the baseline. From query logs, we discover that hot queries include keywords related to academics, occupations and companies. Experiments on those queries show that PageRank-like algorithms perform best for occupation-related queries, forward-relationship-like algorithms work best for academic-related queries and domain-related headcount algorithms work best for company-related queries.     

Computational social choice for coordination in agent networks

This paper presents some models and algorithms for social choice in agent networks. Agent networks are graphical models used to represent systems involving multiple, locally interacting, agents. They allow the representation of complex decision-making situations where the utility function of every agent depends on the actions of its neighbors. In this context, coordination requires some optimization method able to determine a combination of individual actions that maximizes social efficiency. We study here the maximization of different social welfare functions covering various attitudes ranging from utilitarianism to egalitarianism. For all these models we propose graph-based algorithms as well as MIP formulations to find optimal sets of actions. We also provide numerical tests to assess their practical efficiency.     

Efficient algorithms for influence maximization in social networks

In recent years, due to the surge in popularity of social-networking web sites, considerable interest has arisen regarding influence maximization in social networks. Given a social network structure, the problem of influence maximization is to determine a minimum set of nodes that could maximize the spread of influences. With a large-scale social network, the efficiency and practicability of such algorithms are critical. Although many recent studies have focused on the problem of influence maximization, these works in general are time-consuming when a social network is large-scale. In this paper, we propose two novel algorithms, CDH-Kcut and Community and Degree Heuristic on Kcut/SHRINK, to solve the influence maximization problem based on a realistic model. The algorithms utilize the community structure, which significantly decreases the number of candidates of influential nodes, to avoid information overlap. The experimental results on both synthetic and real datasets indicate that our algorithms not only significantly outperform the state-of-the-art algorithms in efficiency but also possess graceful scalability.