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.     

Mining tag-clouds to improve social media recommendation

Multimedia Tools and Applications - Massive amounts of data are available on social websites, therefore finding the suitable item is a challenging issue. According to recent social statistics, we...     

Detecting adverse drug reactions from social media based on multi-channel convolutional neural networks

Neural Computing and Applications - As one of the most important medical field subjects, adverse drug reaction seriously affects the patient’s life, health, and safety. Although many methods...     

Information sharing on social media sites

This study reports on an exploratory survey conducted to investigate the use of social media technologies for sharing information. This paper explores the issue of credibility of the information shared in the context of computer-mediated communication. Four categories of information were explored: sensitive, sensational, political and casual information, across five popular social media technologies: social networking sites, micro-blogging sites, wikis, online forums, and online blogs. One hundred and fourteen active users of social media technologies participated in the study. The exploratory analysis conducted in this study revealed that information producers use different cues to indicate credibility of the information they share on different social media sites. Organizations can leverage findings from this study to improve targeted engagement with their customers. The operationalization of how information credibility is codified by information producers contributes to knowledge in social media research.     

Detecting and responding to hostile disinformation activities on social media using machine learning and deep neural networks

Neural Computing and Applications - Disinformation attacks that make use of social media platforms, e.g., the attacks orchestrated by the Russian “Internet Research Agency” during the...     

The diversity of why: a meta-analytical study of usage motivation in enterprise social networks

In times of demographic change, skill shortage and disruptive innovations, organizational knowledge management and innovative capacity are the key to a company’s success. But how can knowledge be retained with fast staff turnover, global project-based work and parental leaves? Using enterprise social media to improve knowledge dissemination at work seems promising, when looking at the success of private social networking sites. In this article we combine ten different empirical studies which investigated different aspects of how user diversity influences the motivation to use social media at work. The emerging meta-study using the DerSimonian–Laird method (total sample size \(N=522\)) analyzes different aspects of user diversity and their correlation with eight motives for SNS usage: information, importance, contact, self-presentation, autonomy, social comparison, and power and control. We found that that the individual achievement motivation correlates positively with the motives importance, power, information and self-presentation. The need for autonomy correlates with openness to new experiences and the need for social comparison with gender and neuroticism. From our findings, we derive practical implications for designing a social networking site for work which fulfills the users’ needs and functions along their motivation.     

Drawing on mobile crowds via social media

Recent years have witnessed the impact of crowdsourcing model, social media, and pervasive computing. We believe that the more significant impact is latent in the convergence of these ideas on the mobile platform. In this paper, we introduce a mobile crowdsourcing platform that is built on top of social media. A mobile crowdsourcing application called UbiAsk is presented as one study case. UbiAsk is designed for assisting foreign visitors by involving the local crowd to answer their image-based questions at hand in a timely fashion. Existing social media platforms are used to rapidly allocate microtasks to a wide network of local residents. The resulting data are visualized using a mapping tool as well as augmented reality (AR) technology, result in a visual information pool for public use. We ran a controlled field experiment in Japan for 6 weeks with 55 participants. The results demonstrated a reliable performance on response speed and response quantity: half of the requests were answered within 10 min, 75% of requests were answered within 30 min, and on average every request had 4.2 answers. Especially in the afternoon, evening and night, nearly 88% requests were answered in average approximately 10 min, with more than 4 answers per request. In terms of participation motivation, we found the top active crowdworkers were more driven by intrinsic motivations rather than any of the extrinsic incentives (game-based incentives and social incentives) we designed.     

A study of homophily on social media

The fact that similarity breeds connections, the principle of homophily, has been well-studied in existing sociology literature. Several studies have observed this phenomenon by conducting surveys on human subjects. These studies have concluded that new ties are formed between similar individuals. This phenomenon has been used to explain several socio-psychological concepts such as, segregation, community development, social mobility, etc. However, due to the nature of these studies and limitations because of involvement of human subjects, conclusions from these studies are not easily extensible in online social media. Social media, which is becoming the infinite space for interactions, has exceeded all the expectations in terms of growth, for reasons beyond human mind. New ties are formed in social media in the same way that they emerge in real-world. However, given the differences between real world and online social media, do the same factors that govern the construction of new ties in real world also govern the construction of new ties in social media? In other words, does homophily exist in social media? In this article, we study this extremely significant question. We propose a systematic approach by studying three online social media sites, BlogCatalog, Last.fm, and LiveJournal and report our findings along with some interesting observations. The results indicate that the influence of interest-based homophily is not a very strong leading factor for constructing new ties specifically in the three social media sites with implications to strategic advertising, recommendations, and promoting applications at large.     

Deep sentiment analysis: Mining the causality between personality-value-attitude for analyzing business ads in social media

IT vendors routinely use social media such as YouTube not only to disseminate their IT product information, but also to acquire customer input efficiently as part of their market research strategies. Customer responses that appear in social media, however, are typically unstructured; thus, a fairly large data set is needed for meaningful analysis. Although identifying customers’ value structures and attitudes may be useful for developing targeted or niche markets, the unstructured and volume-heavy nature of customer data prohibits efficient and economical extraction of such information. Automatic extraction of customer information would be valuable in determining value structure and strength. This paper proposes an intelligent method of estimating causality between user profiles, value structures, and attitudes based on the replies and published content managed by open social network systems such as YouTube. To show the feasibility of the idea proposed in this paper, information richness and agility are used as underlying concepts to create performance measures based on media/information richness theory. The resulting deep sentiment analysis proves to be superior to legacy sentiment analysis tools for estimation of causality among the focal parameters.     

Improving media services on P2P networks

The Media Accelerating Peer Services system extends P2P infrastructures to improve multimedia services across heterogeneous computing platforms. In this article, we present an architecture and resource management and adaptation framework that transcends existing infrastructures to accommodate and accelerate multimedia peer applications and services. We also propose key technology components that support seamless adaptation of resources to enhance quality of service and the building of better tools and applications that utilize the peer-computing network's underlying power     

Characteristics of social media stories

An emerging trend in social media is for users to create and publish “stories”, or curated lists of  Web resources, with the purpose of creating a particular narrative of interest to the user. While some stories on the Web are automatically generated, such as Facebook’s “Year in Review”, one of the most popular storytelling services is “Storify”, which provides users with curation tools to select, arrange, and annotate stories with content from social media and the Web at large. We would like to use tools, such as Storify, to present (semi-)automatically created summaries of archival collections. To support automatic story creation, we need to better understand as a baseline the structural characteristics of popular (i.e., receiving the most views) human-generated stories. We investigated 14,568 stories from Storify, comprising 1,251,160 individual resources, and found that popular stories (i.e., top 25 % of views normalized by time available on the Web) have the following characteristics: 2/28/1950 elements (min/median/max), a median of 12 multimedia resources (e.g., images, video), 38 % receive continuing edits, and 11 % of their elements are missing from the live Web. We also checked the population of Archive-It collections (3109 collections comprising 305,522 seed URIs) for better understanding the characteristics of the collections that we intend to summarize. We found that the resources in human-generated stories are different from the resources in Archive-It collections. In summarizing a collection, we can only choose from what is archived (e.g., twitter.com is popular in Storify, but rare in Archive-It). However, some other characteristics of human-generated stories will be applicable, such as the number of resources.     

A note on competitive diffusion through social networks

We introduce a game-theoretic model of diffusion of technologies, advertisements, or influence through a social network. The novelty in our model is that the players are interested parties outside the network. We study the relation between the diameter of the network and the existence of pure Nash equilibria in the game. In particular, we show that if the diameter is at most two then an equilibrium exists and can be found in polynomial time, whereas if the diameter is greater than two then an equilibrium is not guaranteed to exist.     

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。     

Rumor spreading in social networks

Social networks are an interesting class of graphs likely to become of increasing importance in the future, not only theoretically, but also for its probable applications to ad hoc and mobile networking. Rumor spreading is one of the basic mechanisms for information dissemination in networks; its relevance stemming from its simplicity of implementation and effectiveness. In this paper, we study the performance of rumor spreading in the classic preferential attachment model of Bollobás et al. which is considered to be a valuable model for social networks. We prove that, in these networks: (a) The standard PUSH-PULL strategy delivers the message to all nodes within O(log²n) rounds with high probability; (b) by themselves, PUSH and PULL require polynomially many rounds. (These results are under the assumption that m, the number of new links added with each new node is at least 2. If m=1 the graph is disconnected with high probability, so no rumor spreading strategy can work.) Our analysis is based on a careful study of some new properties of preferential attachment graphs which could be of independent interest.     

The Web and social networks

The sheer volume of Web data, together with its low signal-to-noise ratio, make it difficult for text-based search engines to locate high-quality pages. Analyzing the links between Web sites has dramatically improved the Web search experience and spawned research into the Web's link structure. This research includes graph-theoretic studies of connectivity, which have shown the Web to have strong similarities with social networks. Self-similarity is pervasive in social networks. While researchers have observed Web self-similarity in other contexts, finding a fractal structure in a graph theoretic setting adds further evidence to the Web's small-world social nature. Thus, researchers seek to explain and exploit the human behavior implicit in the Web's evolving structure. How can we combine the power of Web networks with networks resulting from other human activity? Accomplishing this goal represents knowledge management's key challenge and opportunity.     

跨社交网络的同一用户识别算法

针对跨社交网络的同一用户识别问题,提出了一种综合用户兴趣、写作风格和档案属性的识别方法.通过在这3种不同的特征维度下分别判定用户关系,然后综合判定结果,提高同一用户识别准确性.其中,用户兴趣分为静态兴趣和动态兴趣,静态兴趣采用TextRank算法从用户背景信息中提取,动态兴趣则利用主题模型从用户发表的文本内容中挖掘出随...