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1.
《Information processing & management》2020,57(6):102317
The problem of social spam detection has been traditionally modeled as a supervised classification problem. Despite the initial success of this detection approach, later analysis of proposed systems and detection features has shown that, like email spam, the dynamic and adversarial nature of social spam makes the performance achieved by supervised systems hard to maintain. In this paper, we investigate the possibility of using the output of previously proposed supervised classification systems as a tool for spammers discovery. The hypothesis is that these systems are still highly capable of detecting spammers reliably even when their recall is far from perfect. We then propose to use the output of these classifiers as prior beliefs in a probabilistic graphical model framework. This framework allows beliefs to be propagated to similar social accounts. Basing similarity on a who-connects-to-whom network has been empirically critiqued in recent literature and we propose here an alternative definition based on a bipartite users-content interaction graph. For evaluation, we build a Markov Random Field on a graph of similar users and compute prior beliefs using a selection of state-of-the-art classifiers. We apply Loopy Belief Propagation to obtain posterior predictions on users. The proposed system is evaluated on a recent Twitter dataset that we collected and manually labeled. Classification results show a significant increase in recall and a maintained precision. This validates that formulating the detection problem with an undirected graphical model framework permits to restore the deteriorated performances of previously proposed statistical classifiers and to effectively mitigate the effect of spam evolution. 相似文献
2.
A considerable number of contributions at the intersection of social media platforms and social entrepreneurship has been witnessed over the past decade. The multi-disciplinary nature of current literature necessitates integrative insights on the topic. We thus undertake a two-pronged approach – bibliometric analysis followed by systematic review – to map the extant literature in a structured, objective, and quantitative way. We contribute to the literature as we (i) offer a classification of the literature in three clusters, namely ‘social media platforms, social collaboration and social marketing’, ‘social media platforms and crowdfunding, and ‘social media platforms and crowdsourcing’; (ii) analyze recent research development in each cluster, methodological development, countries co-authorship and evolution of research in the area; and (iii) propose a conceptual framework -accompanied by research propositions- that provides an overview of the literature at the intersection of social media platforms, various social entrepreneurial practices and their influence on the social enterprise performance. Our review culminates with numerous impactful research avenues for scholarly progression in the area. From a practical standpoint, this review integrates scattered findings into one body, allowing the practitioners and policymakers to discern the role of social media platforms in dealing with emerging societal problems and increasing operational efficiencies of social enterprises (SEs). To the best of our knowledge, this is the first review of its kind, offering unique perspectives at the nexus of social media platforms and SE’s performance. 相似文献
3.
Micro-blogging services such as Twitter allow anyone to publish anything, anytime. Needless to say, many of the available contents can be diminished as babble or spam. However, given the number and diversity of users, some valuable pieces of information should arise from the stream of tweets. Thus, such services can develop into valuable sources of up-to-date information (the so-called real-time web) provided a way to find the most relevant/trustworthy/authoritative users is available. Hence, this makes a highly pertinent question for which graph centrality methods can provide an answer. In this paper the author offers a comprehensive survey of feasible algorithms for ranking users in social networks, he examines their vulnerabilities to linking malpractice in such networks, and suggests an objective criterion against which to compare such algorithms. Additionally, he suggests a first step towards “desensitizing” prestige algorithms against cheating by spammers and other abusive users. 相似文献
4.
The use of social media and Web 2.0 platforms is proliferating and affecting different formal and highly structured organisations including public safety agencies. Much of the research in the area has focussed on public use of social media during an emergency as well as how emergency agencies benefit from the data and information generated by this process. However, there is little understanding of “what are the operational implications of this public use on emergency management agencies and how does social media either positively or negatively impact these operations”? In order to progress research into this topic, we chose an engaged scholarship framework to shape a research agenda with the active participation of stakeholders. Hence, we conducted a series of workshops primarily involving over 100 public safety practitioners working in the area of disasters and emergency management who work in public safety agencies, humanitarian organisations, volunteering online platforms and volunteer groups in addition to 20 academics working on this area of enquiry. The findings highlight six different challenges that emergency responding organisations currently face in relation to social media use. We conceptualise these challenges as creating six operational tension zones for organisations. We discuss these tensions and their implications for future research and practice. 相似文献
5.
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
6.
《Information processing & management》2022,59(2):102812
This study addresses the usage of different features to complement synset-based and bag-of-words representations of texts in the context of using classical ML approaches for spam filtering (Ferrara, 2019). Despite the existence of a large number of complementary features, in order to improve the applicability of this study, we have selected only those that can be computed regardless of the communication channel used to distribute content. Feature evaluation has been performed using content distributed through different channels (social networks and email) and classifiers (Adaboost, Flexible Bayes, Naïve Bayes, Random Forests, and SVMs). The results have revealed the usefulness of detecting some non-textual entities (such as URLs, Uniform Resource Locators) in the addressed distribution channels. Moreover, we also found that compression properties and/or information regarding the probability of correctly guessing the language of target texts could be successfully used to improve the classification in a wide range of situations. Finally, we have also detected features that are influenced by specific fashions and habits of users of certain Internet services (e.g. the existence of words written in capital letters) that are not useful for spam filtering. 相似文献
7.
《Information processing & management》2020,57(4):102249
In recent years, most content-based spam filters have been implemented using Machine Learning (ML) approaches by means of token-based representations of textual contents. After introducing multiple performance enhancements, the impact has been virtually irrelevant. Recent studies have introduced synset-based content representations as a reliable way to improve classification, as well as different forms to take advantage of semantic information to address problems, such as dimensionality reduction.These preliminary solutions present some limitations and enforce simplifications that must be gradually redefined in order to obtain significant improvements in spam content filtering. This study addresses the problem of feature reduction by introducing a new semantic-based proposal (SDRS) that avoids losing knowledge (lossless). Synset-features can be semantically grouped by taking advantage of taxonomic relations (mainly hypernyms) provided by BabelNet ontological dictionary (e.g. “Viagra” and “Cialis” can be summarized into the single features “anti-impotence drug”, “drug” or “chemical substance” depending on the generalization of 1, 2 or 3 levels).In order to decide how many levels should be used to generalize each synset of a dataset, our proposal takes advantage of Multi-Objective Evolutionary Algorithms (MOEA) and particularly, of the Non-dominated Sorting Genetic Algorithm (NSGA-II). We have compared the performance achieved by a Naïve Bayes classifier, using both token-based and synset-based dataset representations, with and without executing dimensional reductions. As a result, our lossless semantic reduction strategy was able to find optimal semantic-based feature grouping strategies for the input texts, leading to a better performance of Naïve Bayes classifiers. 相似文献
8.
Blogging has been an emerging media for people to express themselves. However, the presence of spam blogs (also known as splogs) may reduce the value of blogs and blog search engines. Hence, splog detection has recently attracted much attention from research. Most existing works on splog detection identify splogs using their content/link features and target on spam filters protecting blog search engines’ index from spam. In this paper, we propose a splog detection framework by monitoring the on-line search results. The novelty of our splog detection is that our detection capitalizes on the results returned by search engines. The proposed method therefore is particularly useful in detecting those splogs that have successfully slipped through the spam filters that are also actively generating spam-posts. More specifically, our method monitors the top-ranked results of a sequence of temporally-ordered queries and detects splogs based on blogs’ temporal behavior. The temporal behavior of a blog is maintained in a blog profile. Given blog profiles, splog detecting functions have been proposed and evaluated using real data collected from a popular blog search engine. Our experiments have demonstrated that splogs could be detected with high accuracy. The proposed method can be implemented on top of any existing blog search engine without intrusion to the latter. 相似文献
9.
《International Journal of Information Management》2017,37(3):179-189
Social commerce mediated by social media and social network platforms has led to the development of new business models in e-commerce and digitized the consumer decision journey. Social interaction is considered as a prerequisite for successful social commerce since consumers now expect an interactive and social experience while making purchase decisions. Drawing on word of mouth (WOM) and observational learning theories, we conceptualize social interactions in social commerce environments into two forms: WOM communication and observing other consumers’ purchases, and examine their impact on consumer purchase intention and actual purchase behavior. Analyzing primary data (n = 217) collected from surveyed active consumers within social commerce sites at two stages (pre-purchase and post-purchase), we found that positive and negative valence WOM, WOM content, and observing other consumers’ purchases significantly affect consumers’ intention to buy a product, thereby increasing the likelihood of actual buying and sharing product information with others on social commerce sites. 相似文献
10.
The increasing need to compete in innovation and the prevalence of IT in social and economic interactions have led to greater globalization in innovation sourcing, particularly through online crowdsourcing platforms. Crowdsourcing platform participation, a phenomenon inadequately covered, is an instance of providing an innovative solution or idea intertwined with personal and social factors that interact to result in a behavior. A better understanding of the impact of social factors and participants’ hedonic, utilitarian, and social motivations can guide the design and management of these crowdsourcing platforms to foster sustained engagement. This study considered the competitive and social nature of these platforms and analyzed participation intentions from a novel standpoint—a combination of motivational and socio-cognitive perspectives and their relationships within two different types of crowdsourcing platforms: Atizo’s third-party-hosted community and Nokia’s brand-hosted IdeasProject community. A comparison of these two types of crowdsourcing platforms for the same activity of ideation at an individual level revealed differences in behavior determinants based on the platform host type, domain specificity, and mechanisms supporting different motives and social factors. 相似文献
11.
Emma Rooksby 《Ethics and Information Technology》2007,9(2):141-152
Much attention has been given in recent years to the moral status of commercial spam. Less attention has been focused on newer,
non-commercial varieties of spam, such as spam from political parties, community sector organizations and governments. This
article makes a start on evaluating the moral status of these non-commercial varieties of spam, drawing on arguments used
to evaluate commercial spam. 相似文献
12.
《International Journal of Information Management》2017,37(3):111-113
In this paper, we focus on understanding the critical role of everyday citizen in planning and policy-related issues in their city enabled by the newly emerging technological platforms such as mobile-based application and internet-based participatory platforms. The interest and capability of citizens to use and benefit from such online platforms that facilitate urban planning has grown in recent times, which makes it important to study the nature of these platforms and civic engagement that is facilitated through them. We study three such platforms and provide policy recommendations to urban planners based on our analyses of the platforms. 相似文献
13.
作为创新驱动发展战略的关键举措之一，新型研发机构在推动地区创新要素的集聚、开放与互融，打开从“科学”到“技术”再到“产品”的通道，促进创新链、产业链和服务链协同发展，支撑引领战略性新兴产业发展等方面被寄予厚望。目前，政府科技经费投入与相关政策支持是新型研发机构建设发展的重要科技资源来源。在当今社会各界日趋关注公共财政配置绩效的情境下，为保障新型研发机构建设目标实现，最大化财政资金的投资绩效，有关省市相继出台了新型研发机构建设与评估管理相关文件。然而，作为“四不像”的科研机构，新型研发机构“如何评”依旧面临诸多困境与挑战,主要表现为价值困境、多元主体困境、个性与共性兼具困境以及体制机制困境。本文基于新型研发机构组织特点，从资源依赖和社会影响力双重视角，建构了新型研发机构从资源投入到社会影响力产生全过程的绩效分析框架，并以上海微工院为例，针对其不同阶段关键绩效特征建构关键绩效指标与评估重点。其中，建设期重点考核政府资源投入、制度建设与资源集聚能力；运营成长期，关注资源集聚能力、技术研发能力、服务能力和产业化能力，并对新型研发机构近期社会影响力进行评估；运营成熟期，重点评估新型研发机构的社会影响力。 相似文献
14.
In recent years, there has been a groundswell of initiatives aimed at providing platforms to share resources among people. Collaborative consumption provides a model for a ‘sharing economy’ where the dominant logic of consumers is resource access rather than ownership. This study examines the nature and development of a variety of collaborative consumption businesses; in particular, we explore how start-up entrepreneurs see the problems of creating a tribal community among customers and users. Interviews were carried out with founders and co-founders of collaborative consumption ventures during 2014–15. The results suggest that these organisations face many common issues. We develop and apply a framework to understand some of these. We find that collaborative consumption entrepreneurs strive to build a tribal community by matching, in an innovative way, supply and demand. This is typically done by co-creating shared commonality, developing scalable electronic platforms, and building trust into platforms using social media to develop proxy social capital. Consequently, by using existing ecosystems of social media, tribal communities can be formed and scaled much more quickly than via traditional marketing approaches. 相似文献
15.
《Information processing & management》2023,60(3):103282
Detecting collusive spammers who collaboratively post fake reviews is extremely important to guarantee the reliability of review information on e-commerce platforms. In this research, we formulate the collusive spammer detection as an anomaly detection problem and propose a novel detection approach based on heterogeneous graph attention network. First, we analyze the review dataset from different perspectives and use the statistical distribution to model each user's review behavior. By introducing the Bhattacharyya distance, we calculate the user-user and product-product correlation degrees to construct a multi-relation heterogeneous graph. Second, we combine the biased random walk strategy and multi-head self-attention mechanism to propose a model of heterogeneous graph attention network to learn the node embeddings from the multi-relation heterogeneous graph. Finally, we propose an improved community detection algorithm to acquire candidate spamming groups and employ an anomaly detection model based on the autoencoder to identify collusive spammers. Experiments show that the average improvements of precision@k and recall@k of the proposed approach over the best baseline method on the Amazon, Yelp_Miami, Yelp_New York, Yelp_San Francisco, and YelpChi datasets are [13%, 3%], [32%, 12%], [37%, 7%], [42%, 10%], and [18%, 1%], respectively. 相似文献
16.
《Information processing & management》2020,57(1):102140
The demand to detect opinionated spam, using opinion mining applications to prevent their damaging effects on e-commerce reputations is on the rise in many business sectors globally. The existing spam detection techniques in use nowadays, only consider one or two types of spam entities such as review, reviewer, group of reviewers, and product. Besides, they use a limited number of features related to behaviour, content and the relation of entities which reduces the detection's accuracy. Accordingly, these techniques mostly exploit synthetic datasets to analyse their model and are not able to be applied in the context of the real-world environment. As such, a novel graph-based model called “Multi-iterative Graph-based opinion Spam Detection” (MGSD) in which all various types of entities are considered simultaneously within a unified structure is proposed. Using this approach, the model reveals both implicit (i.e., similar entity's) and explicit (i.e., different entities’) relationships. The MGSD model is able to evaluate the ‘spamicity’ effects of entities more efficiently given it applies a novel multi-iterative algorithm which considers different sets of factors to update the spamicity score of entities. To enhance the accuracy of the MGSD detection model, a higher number of existing weighted features along with the novel proposed features from different categories were selected using a combination of feature fusion techniques and machine learning (ML) algorithms. The MGSD model can also be generalised and applied in various opinionated documents due to employing domain independent features. The output of the MGSD model showed that our feature selection and feature fusion techniques showed a remarkable improvement in detecting spam. The findings of this study showed that MGSD could improve the accuracy of state-of-the-art ML and graph-based techniques by around 5.6% and 4.8%, respectively, also achieving an accuracy of 93% for the detection of spam detection in our synthetic crowdsourced dataset and 95.3% for Ott's crowdsourced dataset. 相似文献
17.
《Information processing & management》2020,57(2):102056
Identifying and extracting user communities is an important step towards understanding social network dynamics from a macro perspective. For this reason, the work in this paper explores various aspects related to the identification of user communities. To date, user community detection methods employ either explicit links between users (link analysis), or users’ topics of interest in posted content (content analysis), or in tandem. Little work has considered temporal evolution when identifying user communities in a way to group together those users who share not only similar topical interests but also similar temporal behavior towards their topics of interest. In this paper, we identify user communities through multimodal feature learning (embeddings). Our core contributions can be enumerated as (a) we propose a new method for learning neural embeddings for users based on their temporal content similarity; (b) we learn user embeddings based on their social network connections (links) through neural graph embeddings; (c) we systematically interpolate temporal content-based embeddings and social link-based embeddings to capture both social network connections and temporal content evolution for representing users, and (d) we systematically evaluate the quality of each embedding type in isolation and also when interpolated together and demonstrate their performance on a Twitter dataset under two different application scenarios, namely news recommendation and user prediction. We find that (1) content-based methods produce higher quality communities compared to link-based methods; (2) methods that consider temporal evolution of content, our proposed method in particular, show better performance compared to their non-temporal counter-parts; (3) communities that are produced when time is explicitly incorporated in user vector representations have higher quality than the ones produced when time is incorporated into a generative process, and finally (4) while link-based methods are weaker than content-based methods, their interpolation with content-based methods leads to improved quality of the identified communities. 相似文献
18.
Seyed Amin Mirlohi Falavarjani Fattane Zarrinkalam Jelena Jovanovic Ebrahim Bagheri Ali A. Ghorbani 《Information processing & management》2019,56(6):102070
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
19.
在界定内容策展概念的基础上,本文对国内外内容策展平台进行总结分析,并以国内策展平台——花瓣网为例,借助MetaStudio和DataScraper工具,对策展活动中策展主题、数据来源以及用户相关行为进行探讨。研究表明,策展数据的来源广泛,策展结果主题化鲜明且融入了个人知识的增值信息,内容策展的实例化已基本实现,但大众与小众主题的被关注度存在显著差异,同时平台中信息交流的表达不同于传统的社交平台,其主要以策展结果的方式展现。 相似文献
20.
《Information processing & management》2016,52(1):129-138
This paper presents a Web intelligence portal that captures and aggregates news and social media coverage about “Game of Thrones”, an American drama television series created for the HBO television network based on George R.R. Martin’s series of fantasy novels. The system collects content from the Web sites of Anglo-American news media as well as from four social media platforms: Twitter, Facebook, Google+ and YouTube. An interactive dashboard with trend charts and synchronized visual analytics components not only shows how often Game of Thrones events and characters are being mentioned by journalists and viewers, but also provides a real-time account of concepts that are being associated with the unfolding storyline and each new episode. Positive or negative sentiment is computed automatically, which sheds light on the perception of actors and new plot elements. 相似文献