Feature analysis of spammers in social networks with active honeypots:a case study of Chinese microblogging networks

In this poster we presented our preliminary work on the study of spammer detection and analysis with 50 active honeypot profiles implemented on Weibo.com and QQ.com microblogging networks. We picked out spammers from legitimate users by manually checking every captured user's microblogs content. We built a spammer dataset for each social network community using these spammer accounts and a legitimate user dataset as well. We analyzed several features of the two user classes and made a comparison on these features, which were found to be useful to distinguish spammers from legitimate users. The followings are several initial observations from our analysis on the features of spammers captured on Weibo.com and QQ.com. ¦The following/follower ratio of spammers is usually higher than legitimate users. They tend to follow a large amount of users in order to gain popularity but always have relatively few followers. ¦There exists a big gap between the average numbers of microblogs posted per day from these two classes. On Weibo.com, spammers post quite a lot microblogs every day, which is much more than legitimate users do; while on QQ.com spammers post far less microblogs than legitimate users. This is mainly due to the different strategies taken by spammers on these two platforms. ¦More spammers choose a cautious spam posting pattern. They mix spam microblogs with ordinary ones so that they can avoid the anti-spam mechanisms taken by the service providers. ¦Aggressive spammers are more likely to be detected so they tend to have a shorter life while cautious spammers can live much longer and have a deeper influence on the network. The latter kind of spammers may become the trend of social network spammer. © 2012 IEEE.

Ingroup categorization affects the structural encoding of other-race faces:evidence from the N170 event-related potential

The current research examined the influence of ingroup/outgroup categorization on brain event-related potentials measured during perceptual processing of own- and other-race faces. White participants performed a sequential matching task with upright and inverted faces belonging either to their own race (White) or to another race (Black) and affiliated with either their own university or another university by a preceding visual prime. Results demonstrated that the right-lateralized N170 component evoked by test faces was modulated by race and by social category: the N170 to own-race faces showed a larger inversion effect (i.e., latency delay for inverted faces) when the faces were categorized as other-university rather than own-university members; the N170 to other-race faces showed no modulation of its inversion effect by university affiliation. These results suggest that neural correlates of structural face encoding (as evidenced by the N170 inversion effects) can be modulated by both visual (racial) and nonvisual (social) ingroup/outgroup status. © 2014 © 2014 Taylor & Francis.

A simple Bayesian modelling approach to event extraction from Twitter

With the proliferation of social media sites, social streams have proven to contain the most up-to-date information on current events. Therefore, it is crucial to extract events from the social streams such as tweets. However, it is not straightforward to adapt the existing event extraction systems since texts in social media are fragmented and noisy. In this paper we propose a simple and yet effective Bayesian model, called Latent Event Model (LEM), to extract structured representation of events from social media. LEM is fully unsupervised and does not require annotated data for training. We evaluate LEM on a Twitter corpus. Experimental results show that the proposed model achieves 83% in F-measure, and outperforms the state-of-the-art baseline by over 7%.© 2014 Association for Computational Linguistics.

Entrepreneurial intention and entrepreneurial behaviour:a social psychological perspective

Increasing the supply of entrepreneurs reduces unemployment and accelerates economic growth (Acs, 2006; Audretsch, 2007; Santarelli et el. 2009; Campbell, 1996; Carree & Thurik, 1996). The supply of entrepreneurs depends on the entrepreneurial intention and activity of the people (Kruger & Brazeal, 1994). Existing behavioural theories explain that entrepreneurial activity is an attitude driven process which is mediated by intention and regulated by behavioural control. These theories are: Theory of Planned Behaviour (Ajzen, 1991; 2002, 2012); Entrepreneurial Event Model (Shapiro & Shokol, 1982), and Social Cognitive Theory (Bandura, 1977; 1986; 2012). Meta-analysis of existing behavioural theories in different fields found that the theories are more effective to analyse behavioural intention and habitual behaviour, but less effective to analyse long-term and risky behaviour (McEachan et al., 2011). The objective of this dissertation is to improve entrepreneurship behaviour theory to advance our understanding of the determinants of the entrepreneurial intention and activity. To achieve this objective we asked three compelling questions in our research. These are: Firstly, why do differences exist in entrepreneurship among age groups. Secondly, how can we improve the theory to analyse entrepreneurial intention and behaviour? And, thirdly, is there any relationship between counterfactual or regretful thinking and entrepreneurial intention? We address these three questions in Chapters 2, 3 and 4 of the dissertation. Earlier studies have identified that there is an inverse U shaped relationship between age and entrepreneurship (Parker, 2004; Hart et al., 2004). In our study, we explain the reasons for this inverse U shape (Chapter 2). To analyse the reasons we use Cognitive Life Cycle theory and Disuse theory. We assume that the stage in the life cycle of an individual moderates the influence of opportunity identification and skill to start a business. In our study, we analyse the moderation effect in early stage entrepreneurship and in serial entrepreneurship. In Chapter 3, the limitations of existing psychological theories are discussed, and a competency value theory of entrepreneurship (CVTE) is proposed to overcome the limitations and extend existing theories. We use a ‘weighted competency’ variable instead of a ‘perceived behavioural control’ variable for the theory of planned behaviour (TPB) and self-efficacy variable for social cognitive theory. Weighted competency is the perceived competency ranking assigned by an individual for his total competencies to be an entrepreneur. The proposed theory was tested in a pilot survey in the UK and in a national adult population survey in a South Asian Country. The results show a significant relationship between competencies and entrepreneurial intention, and weighted competencies and entrepreneurial behaviour as per CVTE. To improve the theory further, in Chapter 4, we test the relationship between counterfactual thinking and entrepreneurial intention. Studies in cognitive psychology identify that ‘upward counterfactual thinking’ influences intention and behaviour (Epstude & Rose, 2008; Smallman & Roese, 2009). Upward counterfactual thinking is regretful thinking for missed opportunities of a problem. This study addresses the question of how an individual’s regretful thinking affects his or her future entrepreneurial career intention. To do so, we conducted a study among students in a business school in the UK, and we found that counterfactual thinking modifies the influence of attitude and opportunity identification in entrepreneurial career intention.

Detection of Logical-and-Probabilistic Correlation in Time Series

An application of the heterogeneous variables system prediction method to solving the time series analysis problem with respect to the sample size is considered in this work. It is created a logical-and-probabilistic correlation from the logical decision function class. Two ways is considered. When the information about event is kept safe in the process, and when it is kept safe in depending process.

Performance management of event processing systems

This thesis is a study of performance management of Complex Event Processing (CEP) systems. Since CEP systems have distinct characteristics from other well-studied computer systems such as batch and online transaction processing systems and database-centric applications, these characteristics introduce new challenges and opportunities to the performance management for CEP systems. Methodologies used in benchmarking CEP systems in many performance studies focus on scaling the load injection, but not considering the impact of the functional capabilities of CEP systems. This thesis proposes the approach of evaluating the performance of CEP engines’ functional behaviours on events and develops a benchmark platform for CEP systems: CEPBen. The CEPBen benchmark platform is developed to explore the fundamental functional performance of event processing systems: filtering, transformation and event pattern detection. It is also designed to provide a flexible environment for exploring new metrics and influential factors for CEP systems and evaluating the performance of CEP systems. Studies on factors and new metrics are carried out using the CEPBen benchmark platform on Esper. Different measurement points of response time in performance management of CEP systems are discussed and response time of targeted event is proposed to be used as a metric for quality of service evaluation combining with the traditional response time in CEP systems. Maximum query load as a capacity indicator regarding to the complexity of queries and number of live objects in memory as a performance indicator regarding to the memory management are proposed in performance management of CEP systems. Query depth is studied as a performance factor that influences CEP system performance.

CEPBen:a benchmark for complex event processing systems

Complex Event processing (CEP) has emerged over the last ten years. CEP systems are outstanding in processing large amount of data and responding in a timely fashion. While CEP applications are fast growing, performance management in this area has not gain much attention. It is critical to meet the promised level of service for both system designers and users. In this paper, we present a benchmark for complex event processing systems: CEPBen. The CEPBen benchmark is designed to evaluate CEP functional behaviours, i.e., filtering, transformation and event pattern detection and provides a novel methodology of evaluating the performance of CEP systems. A performance study by running the CEPBen on Esper CEP engine is described and discussed. The results obtained from performance tests demonstrate the influences of CEP functional behaviours on the system performance. © 2014 Springer International Publishing Switzerland.

EPCIS Event-Based traceability in pharmaceutical supply chains via automated generation of linked pedigrees

In this paper we show how event processing over semantically annotated streams of events can be exploited, for implementing tracing and tracking of products in supply chains through the automated generation of linked pedigrees. In our abstraction, events are encoded as spatially and temporally oriented named graphs, while linked pedigrees as RDF datasets are their specific compositions. We propose an algorithm that operates over streams of RDF annotated EPCIS events to generate linked pedigrees. We exemplify our approach using the pharmaceuticals supply chain and show how counterfeit detection is an implicit part of our pedigree generation. Our evaluation results show that for fast moving supply chains, smaller window sizes on event streams provide significantly higher efficiency in the generation of pedigrees as well as enable early counterfeit detection.

A computational approach to measuring the correlation between expertise and social media influence for celebrities on microblogs

Social media influence analysis, sometimes also called authority detection, aims to rank users based on their influence scores in social media. Existing approaches of social influence analysis usually focus on how to develop effective algorithms to quantize users’ influence scores. They rarely consider a person’s expertise levels which are arguably important to influence measures. In this paper, we propose a computational approach to measuring the correlation between expertise and social media influence, and we take a new perspective to understand social media influence by incorporating expertise into influence analysis. We carefully constructed a large dataset of 13,684 Chinese celebrities from Sina Weibo (literally ”Sina microblogging”). We found that there is a strong correlation between expertise levels and social media influence scores. Our analysis gave a good explanation of the phenomenon of “top across-domain influencers”. In addition, different expertise levels showed influence variation patterns: e.g., (1) high-expertise celebrities have stronger influence on the “audience” in their expertise domains; (2) expertise seems to be more important than relevance and participation for social media influence; (3) the audiences of top expertise celebrities are more likely to forward tweets on topics outside the expertise domains from high-expertise celebrities.

Early Detection of Emergent Events Based on an Extremal Process Approach

2000 Mathematics Subject Classification: Primary 60G70, 62F03.

Harnessing linked knowledge sources for topic classification in social media

Topic classification (TC) of short text messages offers an effective and fast way to reveal events happening around the world ranging from those related to Disaster (e.g. Sandy hurricane) to those related to Violence (e.g. Egypt revolution). Previous approaches to TC have mostly focused on exploiting individual knowledge sources (KS) (e.g. DBpedia or Freebase) without considering the graph structures that surround concepts present in KSs when detecting the topics of Tweets. In this paper we introduce a novel approach for harnessing such graph structures from multiple linked KSs, by: (i) building a conceptual representation of the KSs, (ii) leveraging contextual information about concepts by exploiting semantic concept graphs, and (iii) providing a principled way for the combination of KSs. Experiments evaluating our TC classifier in the context of Violence detection (VD) and Emergency Responses (ER) show promising results that significantly outperform various baseline models including an approach using a single KS without linked data and an approach using only Tweets. Copyright 2013 ACM.

An empirical study on uncertainty identification in social media context

Uncertainty text detection is important to many social-media-based applications since more and more users utilize social media platforms (e.g., Twitter, Facebook, etc.) as information source to produce or derive interpretations based on them. However, existing uncertainty cues are ineffective in social media context because of its specific characteristics. In this paper, we propose a variant of annotation scheme for uncertainty identification and construct the first uncertainty corpus based on tweets. We then conduct experiments on the generated tweets corpus to study the effectiveness of different types of features for uncertainty text identification. © 2013 Association for Computational Linguistics.

Social resources in local development. Conference proceedings

It was the intention of the Institute for Sociology and Social Policy at the Corvinus University Budapest in collaboration with the University of Trento to reflect the breadth of local development studies in the conference it staged in June 2011 ‘Social Resources in Local Development’. The event was in the framework of ‘Efficient Government, Professional Public Administration and Regional Development for a Competitive Society’ which is part of the TAMOP Project 4.2.1/B-09/1/KMR – 2010-0005 (Social and Cultural Resources Development Policies and Local Development Research Workshop led by Professor Zoltán Szántó). All the papers which feature in this volume were presented at the workshop. The research activity behind these papers (with the exception of Blokker) was supported by the aforementioned TAMOP project. The event was attended by a range of international participants including Erasmus Mundus students from the University of Trento and Corvinus students taking the MA in Local Development. It is hoped that this publication of conference proceedings highlights some current and key issues and controversies in local development research and academic debate.

The effect of cognitive load on deception

The current study applied classic cognitive capacity models to examine the effect of cognitive load on deception. The study also examined whether the manipulation of cognitive load would result in the magnification of differences between liars and truth-tellers. In the first study, 87 participants engaged in videotaped interviews while being either deceptive or truthful about a target event. Some participants engaged in a concurrent secondary task while being interviewed. Performance on the secondary task was measured. As expected, truth tellers performed better on secondary task items than liars as evidenced by higher accuracy rates. These results confirm the long held assumption that being deceptive is more cognitively demanding than being truthful. In the second part of the study, the videotaped interviews of both liars and truth-tellers were shown to 69 observers. After watching the interviews, observers were asked to make a veracity judgment for each participant. Observers made more accurate veracity judgments when viewing participants who engaged in a concurrent secondary task than when viewing those who did not. Observers also indicated that participants who engaged in a concurrent secondary task appeared to think harder than participants who did not. This study provides evidence that engaging in deception is more cognitively demanding than telling the truth. As hypothesized, having participants engage in a concurrent secondary task led to the magnification of differences between liars and truth tellers. This magnification of differences led to more accurate veracity rates in a second group of observers. The implications for deception detection are discussed.