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<正>产品Twitter可连接苹果音乐社交网络Ping微博网站Twitter于11月中旬表示,该公司的服务将与苹果音乐社交网络Ping相连接。苹果9月份公布了Ping。Ping是苹果的iTunes音乐软件的一部分,用户可以关注歌手,向好友推荐音乐。Twitter表示,逾1.75亿注册用户可     

深入分析Twitter：聚焦和扩散

Twitter目前在国外非常流行，一系列热点事件都跟Twitter紧密相联，如前段时间，德国总统选举中就发生了选举结果提前泄露的“Twitter门”事件。在今年6月份的伊朗大选，Twitter被认为是策划＼煽动伊朗社会动荡的重要推手。美国防部长罗伯特·盖茨就坦言，Twitter等社交媒体网络是美国“极为重要的战略资产”，     

社交网Twitter平台的人物关系网社区发现

社交网络服务（social networking service,SNS）已融入到大众生活中。人们将自己的信息上传到网络中,并通过社交网站管理自己的社交圈子,由此造成大量的个人信息在社交网络上被公开。文章基于Twitter平台,设计实现了Twitter用户关系网的社区发现。通过实时采集Twitter用户信息,重建人物关系网,改进Newman快速算法划分社区发现人物关系网。文章通过可视化的界面呈现用户的社区关系,提供用户网络行为,为决策者的舆情监控或个性推荐提供了参考凭据。     

“微”言耸听——国内外主流微博网站对比与评测

2007年,一项名为Twitter的网络服务出现在互联网上,凭借简明直观的内容在很短的时间内便成为了热门的焦点,吸引了众多网络用户。随着Twitter的流行,互联网上诞生了一个崭新的名词——“微博”。     

微博社交网络的对称程度实证分析

Twitter和Sina微博注册用户构成关注关系社交网络,运用一种对称程度来研究其对称性随社交圈子规模变化的规律。首先根据收集的100万条新浪用户之间的关注关系和236个Twitter用户及其之间的关注关系来构建初始社交网络,选取其中具有明显对称性的连通子网络作为研究的主要对象,通过去除法得到:影响社交网络最大连通子网络对称性的主要因素是大V用户和可忽略用户。其次,采用比较分析法得出Twitter的大V用户构成的社交子网络对称性较强。最后,从功能定位方面分析了两种微博的不同;通过对初始网络的所有连通子网络的对称程度的研究,得出社交圈规模越小、相应的对称性越强的结论。     

企业恋上社交网络

社交网络从无到有 纽约人寿保险有限公司最近在其Twitter上发表了一条消息：人寿保险宣传月的女发言人Leslie Bibb参与了一系列广受欢迎的电影演出（比如你可以在《塔拉迪加之夜》中发现她）。作为一个Twitter包装计划的一部分，纽约人寿还邀请其Twitter跟随者秀出自己生活中真实的故事，主题就是“纽约人寿给自...     

企业恋上社交网络

社交网络从无到有 纽约人寿保险有限公司最近在其Twitter上发表了一条消息：人寿保险宣传月的女发言人Leslie Bibb参与了一系列广受欢迎的电影演出（比如你可以在《塔拉迪加之夜》中发现她）。作为一个Twitter包装计划的一部分，纽约人寿还邀请其Twitter跟随者秀出自己生活中真实的故事，主题就是“纽约人寿给自己和家庭带来的积极影响”。     

“微博化”助推移动SNS前期发展

以Twitter为代表的微博客,无疑是这一年来互联网界最耀眼的明星。成立仅3年,Twitter已蹿升为仅次于Facebook和MySpace的第三大社交网站,其用户不仅有奥巴马、施瓦辛格等政要,有奥尼尔、黛米·摩尔等体育娱乐界明星,还有千千万万普通市民。在Twitter上,朋友、同事和名人的即时信息相互流通,构成了一个巨大的延伸社交网络。     

卷积神经网络下的Twitter文本情感分析

随着社交网络的日益普及,基于Twitter文本的情感分析成为近年来的研究热点。Twitter文本中蕴含的情感倾向对于挖掘用户需求和对重大事件的预测具有重要意义。但由于Twitter文本短小和用户自身行为存在随意性等特点,再加之现有的情感分类方法大都基于手工制作的文本特征,难以挖掘文本中隐含的深层语义特征,因此难以提高情感分类性能。本文提出了一种基于卷积神经网络的Twitter文本情感分类模型。该模型利用word2vec方法初始化文本词向量,并采用CNN模型学习文本中的深层语义信息,从而挖掘Twitter文本的情感倾向。实验结果表明,采用该模型能够取得82.3%的召回率,比传统分类方法的分类性能有显著提高。     

Twitter之外的世界更精彩变身“碎碎念”顽主

Twitter（中文也可称之为唠叨、碎碎念之类）作为最具创意的Web2．0服务，凭借自身魅力引得很多第三方网站甘为其提供外延服务。而且在其热潮漫延至国内之后，又衍生出了各种改良版Twitter。而无论你是小菜还是大虾，只要是年轻人都必然会对这个引领网络潮流的玩意感兴趣，下面我们就来进入游离于Twitter本尊之外的“碎碎念”世界!     

A novel sampling-based visual topic models with computational intelligence for big social health data clustering

The Journal of Supercomputing - Twitter is a popular social network for people to share views or opinions on various topics. Many people search for health topics through Twitter; thus, obtaining a...     

Are you connected? Evaluating information cascades in online discussion about the #RaceTogether campaign

In the context of online discussion about the recent Starbucks' “Race Together” cup campaign, this study aims to explore the central users in the online discussion network on Twitter and the factors contributing to a user's central status in the network. A social network analysis of 18,000 unique tweets comprising 26,539 edges and 14,343 Twitter users indicated five types of central users: conversation starter, influencer, active engager, network builder, and information bridge. Moreover, path analysis revealed that the number of people a Twitter user follows, the number of followers a user has, and the number of tweets a user generates within a time period helped a user increase his/her “indegree” connections in the network, which, together with one's “out-degree” connections in the network, propelled a user to become a central figure in the network.     

Home location profiling for users in social media

In this paper, we focus on the problem of estimating the home locations of users in the Twitter network. We propose a Social Tie Factor Graph (STFG) model to estimate a Twitter user's city-level location based on the user's following network, user-centric data, and tie strength. In STFG, relationships between users and locations are modeled as nodes, while attributes and correlations are modeled as factors. An efficient algorithm is proposed to learn model parameters and predict unknown relationships. We evaluate our proposed method by investigating Twitter networks. The experimental results demonstrate that our proposed method significantly outperforms several state-of-the-art methods.     

How Social Network Site Users’ Motives Predict Their Online Network Sizes: A Quantile Regression Approach to Japanese Twitter Usage

This study examines the relationships between Twitter users’ motives for using the service and their egocentric network sizes on Twitter in terms of online social capital. Based on the literature, we focus on quantiles of egocentric network sizes rather than on means. The respondents were 1,559 Japanese Twitter users; they participated in an online survey and allowed us to collect their log data on Twitter. A socializing motive was associated with the number of mutual follows only in the lower tails of the size distribution and was negatively linked to the number of one-sided follows. In contrast, an information-seeking motive was positively related to the number of one-sided follows. These findings suggest that cognitive constraints exert an effect on socializing through an online service.     

Peri-Watchdog: Hunting for hidden botnets in the periphery of online social networks

In order to evade detection of ever-improving defense techniques, modern botnet masters are constantly looking for new communication platforms for delivering C&C (Command and Control) information. Attracting their attention is the emergence of online social networks such as Twitter, as the information dissemination mechanism provided by these networks can naturally be exploited for spreading botnet C&C information, and the enormous amount of normal communications co-existing in these networks makes it a daunting task to tease out botnet C&C messages.Against this backdrop, we explore graph-theoretic techniques that aid effective monitoring of potential botnet activities in large open online social networks. Our work is based on extensive analysis of a Twitter dataset that contains more than 40 million users and 1.4 billion following relationships, and mine patterns from the Twitter network structure that can be leveraged for improving efficiency of botnet monitoring. Our analysis reveals that the static Twitter topology contains a small-sized core sugraph, after removing which, the Twitter network breaks down into small connected components, each of which can be handily monitored for potential botnet activities. Based on this observation, we propose a method called Peri-Watchdog, which computes the core of a large online social network and derives the set of nodes that are likely to pass botnet C&C information in the periphery of online social network. We analyze the time complexity of Peri-Watchdog under its normal operations. We further apply Peri-Watchdog on the Twitter graph injected with synthetic botnet structures and investigate the effectiveness of Peri-Watchdog in detecting potential C&C information from these botnets.To verify whether patterns observed from the static Twitter graph are common to other online social networks, we analyze another online social network dataset, BrightKite, which contains evolution of social graphs formed by its users in half a year. We show not only that there exists a similarly relatively small core in the BrightKite network, but also this core remains stable over the course of BrightKite evolution. We also find that to accommodate the dynamic growth of BrightKite, the core has to be updated about every 18 days under a constrained monitoring capacity.     

Twitter spam account detection based on clustering and classification methods

The Journal of Supercomputing - Twitter social network has gained more popularity due to the increase in social activities of registered users. Twitter performs dual functions of online social...     

Twitter Data Analysis Using Hadoop and ‘R’ and Emotional Analysis Using Optimized SVNN

Standalone systems cannot handle the giant traffic loads generated by Twitter due to memory constraints. A parallel computational environment provided by Apache Hadoop can distribute and process the data over different destination systems. In this paper, the Hadoop cluster with four nodes integrated with RHadoop, Flume, and Hive is created to analyze the tweets gathered from the Twitter stream. Twitter stream data is collected relevant to an event/topic like IPL- 2015, cricket, Royal Challengers Bangalore, Kohli, Modi, from May 24 to 30, 2016 using Flume. Hive is used as a data warehouse to store the streamed tweets. Twitter analytics like maximum number of tweets by users, the average number of followers, and maximum number of friends are obtained using Hive. The network graph is constructed with the user’s unique screen name and mentions using ‘R’. A timeline graph of individual users is generated using ‘R’. Also, the proposed solution analyses the emotions of cricket fans by classifying their Twitter messages into appropriate emotional categories using the optimized support vector neural network (OSVNN) classification model. To attain better classification accuracy, the performance of SVNN is enhanced using a chimp optimization algorithm (ChOA). Extracting the users’ emotions toward an event is beneficial for prediction, but when coupled with visualizations, it becomes more powerful. Bar-chart and wordcloud are generated to visualize the emotional analysis results.     

Tweeting to Feel Connected: A Model for Social Connectedness in Online Social Networks

Social connectedness is an indicator of the extent to which people can realize various network benefits and is therefore a source of social capital. Using the case of Twitter, a theoretical model of social connectedness based on the functional and structural characteristics of people's communication behavior within an online social network is developed and tested. The study investigates how social presence, social awareness, and social connectedness influence each other, and when and for whom the effects of social presence and social awareness are most strongly related to positive outcomes in social connectedness. Specifically, the study looks at the concurrent direct and moderating effect of two structural constructs characterizing people's online social network: network size and frequency of usage. The research model is tested using data (n?=?121) collected from two sources: (a) an online survey of Twitter users and (b) their usage data collected directly from Twitter. Results indicate that social awareness, social presence, and usage frequency have a direct effect on social connectedness, whereas network size has a moderating effect. Social presence is found to partially mediate the relationship between social awareness and social connectedness. The findings of the analysis are used to outline design implications for online social networks from a human–computer interaction perspective.     

Who is Who on Twitter–Spammer,Fake or Compromised Account? A Tool to Reveal True Identity in Real-Time

Social networks once being an innoxious platform for sharing pictures and thoughts among a small online community of friends has now transformed into a powerful tool of information, activism, mobilization, and sometimes abuse. Detecting true identity of social network users is an essential step for building social media an efficient channel of communication. This paper targets the microblogging service, Twitter, as the social network of choice for investigation. It has been observed that dissipation of pornographic content and promotion of followers market are actively operational on Twitter. This clearly indicates loopholes in the Twitter’s spam detection techniques. Through this work, five types of spammers-sole spammers, pornographic users, followers market merchants, fake, and compromised profiles have been identified. For the detection purpose, data of around 1 Lakh Twitter users with their 20 million tweets has been collected. Users have been classified based on trust, user and content based features using machine learning techniques such as Bayes Net, Logistic Regression, J48, Random Forest, and AdaBoostM1. The experimental results show that Random Forest classifier is able to predict spammers with an accuracy of 92.1%. Based on these initial classification results, a novel system for real-time streaming of users for spam detection has been developed. We envision that such a system should provide an indication to Twitter users about the identity of users in real-time.