首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 859 毫秒
1.
This paper investigates the research question if senders of large amounts of irrelevant or unsolicited information – commonly called “spammers” – distort the network structure of social networks. Two large social networks are analyzed, the first extracted from the Twitter discourse about a big telecommunication company, and the second obtained from three years of email communication of 200 managers working for a large multinational company. This work compares network robustness and the stability of centrality and interaction metrics, as well as the use of language, after removing spammers and the most and least connected nodes. The results show that spammers do not significantly alter the structure of the information-carrying network, for most of the social indicators. The authors additionally investigate the correlation between e-mail subject line and content by tracking language sentiment, emotionality, and complexity, addressing the cases where collecting email bodies is not permitted for privacy reasons. The findings extend the research about robustness and stability of social networks metrics, after the application of graph simplification strategies. The results have practical implication for network analysts and for those company managers who rely on network analytics (applied to company emails and social media data) to support their decision-making processes.  相似文献   

2.
魏静  黄阳江豪  朱恒民 《现代情报》2019,39(10):110-118
[目的]为了研究社交网络以及研究微博网络与微信网络之间舆情的传递过程。[方法]利用无标度有向网络和BA网络分别模拟微博网络和微信网络环境,通过特定的连接关系设计了耦合网络载体,在SEIR模型的基础上,充分分析了用户的传播心理,考虑到了个体具有兴趣衰减效应以及记忆效应等特征,构建了基于耦合网络的社交网络舆情传播模型。[结果]实验结果表明,构建的双层社交网络舆情传播模型能较好地反映现实生活中的舆情传播过程,用户在多层社交网络之间的互动加速了舆情信息的流动,扩大了舆情信息的影响力,层间传播阈值的控制是管理多层社交网络舆情传播的关键。  相似文献   

3.
Increased usage of bots through the Internet in general, and social networks in particular, has many implications related to influencing public opinion. Mechanisms to distinguish humans from machines span a broad spectrum of applications and hence vary in their nature and complexity. Here we use several public Twitter datasets to build a model that can predict whether or not an account is a bot account based on features extracted at the tweet or the account level. We then apply the model to Twitter's Russian Troll Tweets dataset. At the account level, we evaluate features related to how often Twitter accounts are tweeting, as previous research has shown that bots are very active at some account levels and very low at others. At the tweet level, we noticed that bot accounts tend to sound more formal or structured, whereas real user accounts tend to be more informal in that they contain more slang, slurs, cursing, and the like. We also noted that bots can be created for a range of different goals (e.g., marketing and politics) and that their behaviors vary based on those distinct goals. Ultimately, for high bot-prediction accuracy, models should consider and distinguish among the different goals for which bots are created.  相似文献   

4.
Users’ ability to retweet information has made Twitter one of the most prominent social media platforms for disseminating emergency information during disasters. However, few studies have examined how Twitter’s features can support the different communication patterns that occur during different phases of disaster events. Based on the literature of disaster communication and Media Synchronicity Theory, we identify distinct disaster phases and the two communication types—crisis communication and risk communication—that occur during those phases. We investigate how Twitter’s representational features, including words, URLs, hashtags, and hashtag importance, influence the average retweet time—that is, the average time it takes for retweet to occur—as well as how such effects differ depending on the type of disaster communication. Our analysis of tweets from the 2013 Colorado floods found that adding more URLs to tweets increases the average retweet time more in risk-related tweets than it does in crisis-related tweets. Further, including key disaster-related hashtags in tweets contributed to faster retweets in crisis-related tweets than in risk-related tweets. Our findings suggest that the influence of Twitter’s media capabilities on rapid tweet propagation during disasters may differ based on the communication processes.  相似文献   

5.
在网络舆情内容的传播过程中,各种物理上独立的舆论会话在传播要素上可能存在着语义关联,并且传播要素之间的相互影响对舆情传播内容的演变具有重要作用。本文从网络舆情的传播阶段中传播要素的相互影响入手,以传播内容为主要研究对象,以社群网络中的关键节点及其传播主题为分析单元,将生命周期理论和关键节点识别相结合,并选择新浪微博作为数据来源,采集舆情事件信息,构建舆情事件生命周期各阶段的社会网络并提取关键节点,借助LDA主题模型方法挖掘各阶段舆情内容的主题,在此基础上研究相同阶段或者不同阶段中在关键节点影响下的舆情主题分布及其变化。研究结论为社会舆情分析与决策支持提供了一定的参考。  相似文献   

6.
A large body of research work has proposed verification techniques for rumors spreading in social media that mainly relied on subjective evidence, e.g., propagation networks or user interactions. Alternatively, in this work, we introduce the task of authority finding in social media, in which we aim to find authorities, for given rumors spreading specifically in Twitter, who can help verify them by providing exclusive/convincing evidence that supports or denies those rumors. We release the first test collection for Authority FINding in Arabic Twitter (AuFIN). The collection comprises 150 rumors (expressed in tweets) associated with a total of 1,044 authority accounts and a user collection of 395,231 Twitter accounts (members of 1,192,284 unique Twitter lists). Moreover, we propose a hybrid model that employs pre-trained language models and combines lexical, semantic, and network signals to find authorities. Our experiments show that the textual representation of users is insufficient, and incorporating the Twitter network features improved the recall of authorities by 34%. Moreover, semantic ranking is inferior to the lexical and network-based ranking in terms of precision, but superior in terms of recall. Therefore, combining both the semantic and network-based ranking achieved the best overall performance achieving a precision of 0.413 and 0.213 at depth 1 and 5 respectively. We show that rumor expansion by exploiting Knowledge Bases improves the recall of authorities by up to 15%. Furthermore, we find that SOTA models for topic expert finding perform poorly on finding authorities. Finally, drawing upon our experiments, we discuss failure factors and make recommendations for future research directions in addressing this task.  相似文献   

7.
The ever increasing presence of online social networks in users’ daily lives has led to the interplay between users’ online and offline activities. There have already been several works that have studied the impact of users’ online activities on their offline behavior, e.g., the impact of interaction with friends on an exercise social network on the number of daily steps. In this paper, we consider the inverse to what has already been studied and report on our extensive study that explores the potential causal effects of users’ offline activities on their online social behavior. The objective of our work is to understand whether the activities that users are involved with in their real daily life, which place them within or away from social situations, have any direct causal impact on their behavior in online social networks. Our work is motivated by the theory of normative social influence, which argues that individuals may show behaviors or express opinions that conform to those of the community for the sake of being accepted or from fear of rejection or isolation. We have collected data from two online social networks, namely Twitter and Foursquare, and systematically aligned user content on both social networks. On this basis, we have performed a natural experiment that took the form of an interrupted time series with a comparison group design to study whether users’ socially situated offline activities exhibited through their Foursquare check-ins impact their online behavior captured through the content they share on Twitter. Our main findings can be summarised as follows (1) a change in users’ offline behavior that affects the level of users’ exposure to social situations, e.g., starting to go to the gym or discontinuing frequenting bars, can have a causal impact on users’ online topical interests and sentiment; and (2) the causal relations between users’ socially situated offline activities and their online social behavior can be used to build effective predictive models of users’ online topical interests and sentiments.  相似文献   

8.
The increased availability of social media big data has created a unique challenge for marketing decision-makers; turning this data into useful information. One of the significant areas of opportunity in digital marketing is influencer marketing, but identifying these influencers from big data sets is a continual challenge. This research illustrates how one type of influencer, the market maven, can be identified using big data. Using a mixed-method combination of both self-report survey data and publicly accessible big data, we gathered 556,150 tweets from 370 active Twitter users. We then proposed and tested a range of social-media-based metrics to identify market mavens. Findings show that market mavens (when compared to non-mavens) have more followers, post more often, have less readable posts, use more uppercase letters, use less distinct words, and use hashtags more often. These metrics are openly available from public Twitter accounts and could integrate into a broad-scale decision support system for marketing and information systems managers. These findings have the potential to improve influencer identification effectiveness and efficiency, and thus improve influencer marketing.  相似文献   

9.
Marketing professionals face challenges of increasing complexity to adapt classic marketing strategies to the phenomenon of social networks. Companies are currently trying to take advantage of the useful collective knowledge available on social networks to support different types of marketing decisions. The appropriate analysis of this information can offer marketing professionals with important competitive advantages. This work proposes a new methodology to extract the social collective behavior of Twitter users concerning a group of brands based on the users’ temporal activity. Time series of mentions made by individual users to each company’s Twitter account are aggregated to obtain collective activity data for the companies, which is a consequence of both the company’s and other users’ actions. These data are processed using classical unsupervised machine learning techniques, such as temporal clustering and hidden Markov models, to extract collective temporal behavior patterns and models of the dynamics of customers over time for a single brand and groups of brands. The derived knowledge can be used for different tasks, such as identifying the impact of a marketing campaign on Twitter and comparatively assessing the social behaviors of different brands and groups of brands to assist in making marketing decisions. Our methodology is validated in a case study from the wine market. Twitter data were gathered from four regions of different countries around the world with important wineries (Italy: Veneto, Portugal: Porto and Douro Valley, Spain: La Rioja, and United States: Napa Valley), and comparative behavior analysis was carried out from the perspective of the use of Twitter as a communication channel for marketing campaigns.  相似文献   

10.
Social media platforms allow users to express their opinions towards various topics online. Oftentimes, users’ opinions are not static, but might be changed over time due to the influences from their neighbors in social networks or updated based on arguments encountered that undermine their beliefs. In this paper, we propose to use a Recurrent Neural Network (RNN) to model each user’s posting behaviors on Twitter and incorporate their neighbors’ topic-associated context as attention signals using an attention mechanism for user-level stance prediction. Moreover, our proposed model operates in an online setting in that its parameters are continuously updated with the Twitter stream data and can be used to predict user’s topic-dependent stance. Detailed evaluation on two Twitter datasets, related to Brexit and US General Election, justifies the superior performance of our neural opinion dynamics model over both static and dynamic alternatives for user-level stance prediction.  相似文献   

11.
Social informatics is the body of research that examines the design, uses, and consequences of information and communication technologies in ways that take into account their interaction with institutional and cultural contexts. This article serves as a brief introduction to social informatics. Examples such as computer networks, scientific communication via electronic journals, and public access to the Internet are used to illustrate key ideas from social informatics research. Some of the key themes include the importance of social contexts and work processes, sociotechnical networks, public access to information, and social infrastructure for computing support. The article draws upon 25 years of systematic analytical and critical research about information technology and social change.  相似文献   

12.
Intergenerational supportive climate, top management support, organizational institution are seen as three types of important organizational factors for intergenerational knowledge transfer (IGKT), however, current studies are qualitative with little empirical evidence, further, the interrelations of them are little known. This paper investigates and verifies their relationships by an empirical study, especially focuses on their differential effects on younger employees’ participation in IGKT using offline versus online communication methods drawing upon social influence theory. A survey with younger doctors was conducted in the medical industry to test the research model proposed in this study. Results show that younger employees’ perceived intergenerational supportive climate (PISC) has a significant positive influence on offline IGKT, but not on online IGKT; perceived top management support (PTMS) has a significant positive influence on offline IGKT, but has a negative impact on online IGKT; perceived organizational institution support (POIS) has a significant positive influence on online IGKT, but not on offline IGKT. These findings contribute to a more comprehensive understanding about IGKT, as well as help managers be more effective to enhance younger employees’ participation in IGKT through offline/online methods, which contribute to offline and online intergenerational knowledge transfer to knowledge management.  相似文献   

13.
Consideration of social media use for issues of risk communication has received rapid attention in the scholarly literature. However, specific features of social media and their relevance for risk communication warrant continued investigation. The current study examines how system-generated cues available in social media impact perceptions of trust at the organizational level. After viewing one of three mock Twitter pages from an organization that varied the number of retweets concerning the risk of contaminated food in grocery stores, participants were asked to report their perceived trust in the organization. Data indicate a reverse bandwagon or snob effect, such that having too many retweets results in lower judgments of organizational trust. Results are discussed in addition to limitations and future directions for research.  相似文献   

14.
The business-IT gap is still present in many companies and IT/IS professionals often impute the responsibility for this to management and claim they lack top management's support for their initiatives. The aim of this paper is to show how IT/IS personnel can achieve top management support. Based on more than 50 in-depth interviews with CIOs and CEOs in the last 10 years we hypothesize that top management support can be attained with the business and managerial knowledge and skills of IT/IS personnel as well as with the business-oriented role of the IT/IS department. The impact was empirically tested via structural equation modeling (SEM) using data from 152 Slovenian companies with more than 50 employees. Based on findings some implications for top managers and IT/IS professionals are given, especially for CIOs, on how IT/IS personnel can contribute to bridging the gap.  相似文献   

15.
Organizations adopt sophisticated management information systems, which provide top managers with an ample range of information to achieve multiple strategic performances. However, organizations differ in the extent to which they improve their performance. This paper analyzes the role of top management team in the relationship between management information systems and strategic performance. Using data collected from 92 top management teams, it analyses how different team compositions interact with a sophisticated management information system, and how this interaction affects strategic performances, which are focused on cost reduction and flexibility. The findings show how the effect of management information system on strategic performance (focused on flexibility) is moderated by top management team diversity.  相似文献   

16.
Recent years have been characterized by the ubiquitous use of social networks as a mean of self and social identity, which offers new opportunities for qualitative and quantitative research in social sciences. The dynamics of interactions on social platforms such as Twitter promote the development of social movements around hashtags, such as #MeToo. According to previous research, this movement has set the beginning of an era. The present study aims to determine the key indicators of social identity in the #MeToo movement in Twitter using textual analysis and sentiment analysis of user-generated content. To this end, we use a cognitive pragmatics point of view to study a corpus of 31.305 tweets. Using the methodological approaches of corpus linguistics (CL) and discourse analysis (DA), we identify keywords, topics, frequency, and n-grams or collocations to understand the social identity of the #MeToo movement. The key indicators of the social identity in the #MeToo Era are validated using association statistical measures of Log-Likelihood and Mutual Information (MI). Our results reveal the polarization of sentiments where UGC is associated with both negative and positive topics. The social identity is particularly strongly correlated with women and the workplace. Finally, regardless the industry or area, these results present a holistic approach to the social identity of #MeToo.  相似文献   

17.
传统传播环境下企业营销传播活动对用户品牌态度形成具有显著影响,但社会化媒体的发展极大地改变了企业营销传播的生态环境,现阶段企业的社会化媒体传播并未获得预期影响力,需要从理论上对企业社会化媒体传播的策略及其影响因素进行创新性研究。本文采用实验研究方法,基于企业传播信息内容主题、信息源、传播策略与用户再传播意愿和品牌态度间关系的理论假设,实证研究发现:企业社会化媒体传播对用户品牌态度有正向显著影响;信息内容主题类型、信息源、传播组合策略对用户再传播意愿有显著影响;用户再传播意愿对用户品牌态度的影响不显著等。研究结论丰富了企业社会化媒体传播的理论研究,对企业社会化媒体传播实践具有指导意义。  相似文献   

18.
向安玲  沈阳 《情报杂志》2021,40(2):131-137
[目的/意义]意见领袖在公共政策网络议程建构、异构和重构过程中发挥着作用,通过动态追踪议程变化可以准确把握政策传播效果。[方法/过程]以全国及重点城市垃圾分类政策为例,基于内容分析方法(Content Analysis)与网络议程设置理论(Network Agenda Setting,NAS),针对微博平台上的政务账号、媒体账号、意见领袖账号和网民账号的议题属性网络进行关联性分析,进一步探讨公共政策议程设置效果及其中介机制。[结果/结论]研究发现,垃圾分类政策传播过程中,政策议程、新媒体议程(包含政务、媒体和意见领袖账号)与普通网民议程呈现出“异构化”属性网络;相比于政务微博账号和媒体微博账号,意见领袖账号在政策传播过程中触达受众广、活跃度高、交互性强、网络议程设置效果更为显著,作为“中继人”的角色最为凸显。  相似文献   

19.
This study aims to find out how different processes of knowledge management and patterns of social networking affect team performance. Our data on teams originate from a sample of different organizations from a variety of both public and private industries in Finland (76 teams; 499 employees). One of the main deficiencies in the current literature on knowledge and networks is that they tend to concentrate on specific types of teams in a single organization context. Our aim was to put the team phenomenon into an everyday context by analysing the interplay of knowledge creation and social networks in teams which function on a permanent basis in a variety of industry contexts. Both knowledge creation and social networking contributed to performance, but the results showed that whereas team members see the knowledge conversion processes as central to performance, top management emphasize the importance of social networks in value creation. In our examination, lively interaction between team members, combined with team leaders’ intra-organizational networks, contributed to team performance.  相似文献   

20.
The purpose of this study is to empirically investigate the relationship between the social capital accumulated among network members and the performance of learning networks in terms of their ability to enhance knowledge sharing among network members. A network level perspective guided the sampling strategy adopted for this survey involving 150 members of 16 European learning networks. Hierarchical multiple regression and structural equation modelling were employed to investigate the inter-relationships between dimensions of social capital and knowledge sharing in learning networks. The results reveal that social interaction and cognitive social capital are positively and significantly related to knowledge sharing in learning networks. Social interaction is also shown to play an important role in the development of shared vision and shared language (i.e. cognitive social capital) in learning networks. This paper sheds further light on the inter-relationships between different dimensions of social capital from a network (rather than firm) level perspective, and contributes to emerging theory on the antecedents to, and assessment of, performance in learning network entities.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号