PIB: Profiling Influential Blogger in Online Social Networks, A Knowledge Driven Data Mining Approach

AbstractOnline Social Networks (OSNs) facilitate to create and spread information easily and rapidly, influencing others to participate and propagandize. This work proposes a novel method of profiling Influential Blogger (IB) based on the activities performed on one's blog documents who influences various other bloggers in Social Blog Network (SBN). After constructing a social blogging site, a SBN is analyzed with appropriate parameters to get the Influential Blog Power (IBP) of each blogger in the network and demonstrate that profiling IB is adequate and accurate. The proposed Profiling Influential Blogger (PIB) Algorithm survival rate of IB is high and stable

A META-ANALYTIC REVIEW OF SOCIAL MEDIA STUDIES

Social media such as social networking sites, blogs, micro-blogs, Wikis, are increasingly and widely used in our daily lives. In the information system (IS) discipline, social media have become a hot research area and draw the attention of many scholars. The paper systematically reviewed social media studies published in Association for Information Systems (AIS) listed top 20 journals from 2009 to 2013. The publication time, journal preferences, research objects and research topics are discussed. Generally, the current social media studies including four areas, namely user, management, technology and information. Each area has distinct focuses and topics. By thoroughly analyzing the research topics, the authors formulate our projections and recommendations for future social media studies

Identifying Infection Sources and Regions in Large Networks

Identifying the infection sources in a network, including the index cases that introduce a contagious disease into a population network, the servers that inject a computer virus into a computer network, or the individuals who started a rumor in a social network, plays a critical role in limiting the damage caused by the infection through timely quarantine of the sources. We consider the problem of estimating the infection sources and the infection regions (subsets of nodes infected by each source) in a network, based only on knowledge of which nodes are infected and their connections, and when the number of sources is unknown a priori. We derive estimators for the infection sources and their infection regions based on approximations of the infection sequences count. We prove that if there are at most two infection sources in a geometric tree, our estimator identifies the true source or sources with probability going to one as the number of infected nodes increases. When there are more than two infection sources, and when the maximum possible number of infection sources is known, we propose an algorithm with quadratic complexity to estimate the actual number and identities of the infection sources. Simulations on various kinds of networks, including tree networks, small-world networks and real world power grid networks, and tests on two real data sets are provided to verify the performance of our estimators

P Deepa Shenoy and Venugopal KR,“PTMIB: Profiling Top Most Influential Blogger using Content Based Data Mining Approach,”

Users of Online Social Network (OSN) communicate with each other, exchange information and spread rapidly influencing others in the network for taking various decisions. Blog sites allow their users to create and publish thoughts on various topics of their interest in the form of blogs/blog documents, catching the attention and letting readers to perform various activities on them. Based on the content of the blog documents posted by the user, they become popular. In this work, a novel method to profile Top Most Influential Blogger (TMIB) is proposed based on content analysis. Content of blog documents of bloggers under consideration in the blog network are compared and analyzed. Term Frequency and Inverse Document Frequency (TF-IDF) of blog documents under consideration are obtained and their Cosine Similarity score is computed. Synonyms are substituted against those unmatched keywords if the Cosine Similarity score so computed is below the threshold and an improved Cosine Similarity score of those documents under consideration is obtained. Computing the Influence Score after Synonym substitution (ISaS) of those bloggers under conflict, the top most influential blogger is profiled. The simulation results demonstrate that the proposed Profiling Top Most Influential Blogger using Synonym Substitution (PTMIBSS) algorithm is adequately accurate in determining the top most influential blogger at any instant of time considered

An overview on user profiling in online social networks

Advances in Online Social Networks is creating huge data day in and out providing lot of opportunities to its users to express their interest and opinion. Due to the popularity and exposure of social networks, many intruders are using this platform for illegal purposes. Identifying such users is challenging and requires digging huge knowledge out of the data being flown in the social media. This work gives an insight to profile users in online social networks. User Profiles are established based on the behavioral patterns, correlations and activities of the user analyzed from the aggregated data using techniques like clustering, behavioral analysis, content analysis and face detection. Depending on application and purpose, the mechanism used in profiling users varies. Further study on other mechanisms used in profiling users is under the scope of future endeavors

Facebook-päivityksen viraaliuteen vaikuttavat ominaisuudet. Tarkastelussa Audin suomalaiset ja yhdysvaltalaiset Facebook-sivut

Tutkimuksen tavoitteena oli selvittää, mitkä ominaisuudet tekevät autoyhtiön Facebook-päivityksistä paljon eteenpäin jaettuja eli viraaleja kahdella hyvin erilaisella markkina-alueella, Suomessa ja Yhdysvalloissa. Aineistoksi valittiin saksalaisen autonvalmistajan Audin suomalaisilla ja amerikkalaisilla Facebook-sivuilla kymmenen kuukauden aikana helmikuusta marraskuuhun vuonna 2013 julkaistut päivitykset. Viraalipäivitykseksi kutsutaan laajasti, viruksenomaisesti internetissä levinnyttä päivitystä. Tässä tutkimuksessa viraalipäivitykseksi tulkittiin Suomen Facebook-sivulla päivitys, jos sitä oli jaettu vähintään 50 kertaa, Yhdysvaltain Audin Facebook-sivun mittakaavassa vastaava raja on 6 000 jakoa. Tavoitteeseen pyrittiin analysoimalla valittua aineistoa ja rinnastamalla sitä markkinointiin, yhtiön brändäykseen ja muuhun aiheeseen liittyvään aiempaan tutkimukseen. Kvantitatiivista sisällönanalyysia käytettiin jaetuimpien viraalipäivitysten rajaamiseen aineistosta. Kvalitatiivisella sisällönanalyysillä etsittiin niiden viraalisuutta selittäviä ominaisuuksia. Aineistosta nousi edelleen vertailussa käytettäviksi ominaisuuksiksi kontrastisuus, eli värien tai asioiden vastakohtaisuus, käyttäjien huomiointi tai kiittäminen, lokalisaatio eli erityispiirteiden lisääminen päivitykseen tietyssä määrätyssä maassa tapahtuvaa käyttöä varten, uuden tekniikan esittely, kehoitus seurata jotakin, parhaisiin hetkiin liittäminen, rohkeuden tai itsevarmuuden lisääminen, lupaus tulevista vastaavista uutisista ja positiivinen käyttäjän arvottaminen. Kummassakin maassa eniten vaikutusta oli sillä, että päivitys oli sovitettu kohdemaahan. Suomessa toiseksi vaikuttavin piirre oli käyttäjien huomiointi tai kiittäminen, USA:n toisen tilan jakoivat käyttäjien huomiointi tai kiittäminen ja uuden tekniikan/tiedon esittely. Kolmannen sijan jakoivat Suomen sivulla kehotus seurata jotakin, kontrasti ja parhaisiin hetkiin liittäminen. USA:n kolmannen sijan jakoivat kehotus seurata jotakin ja kontrasti. Tutkimuksen lopputulos osoittaa, että sijoittamalla päivitykseen oikeanlaisia ominaisuuksia, voidaan lisätä mahdollisuutta, että päivityksestä tulee viraali.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

E-book adoption in academic and research libraries

Electronic books (e-books) have grown in importance in Academic and Research Libraries (ARLs). Some ARLs are now spending more on e-book acquisitions than hardcopy books. Whether this investment in e-book provision is justified by adoption outcomes is often the subject of simplistic, rather than rigorous research. This research has attempted to rigorously explore the phenomenon of e-book adoption in a case study ARL, namely, Edith Cowan University (ECU) Library. The study population consisted of ECU academics, students and non-academic staff. The research had three aims. First, by employing a theoretical framework based on technology adoption and information behaviour theory, the study sought explanations of adoption behaviours in the population. In a triangular research design, that included a survey, ECU users were invited to self-describe their own patterns of e-book behaviour. Survey data was used to determine if behaviour observed in transactions could be explained in terms of constructs derived from technology acceptance, information behaviour and other domain theories that seek to understand user interaction with information sources. Next, applying log analysis techniques to system-generated datasets of e-book usage, the researcher documented and analysed patterns of ECU e-book user behaviour in terms of the transaction record. Lastly, the study investigated whether transaction logs could be used with high reliability to profile users’ information behaviour providing the basis of a method for e-book individualisation. The study attempted to profile power users and to derive a predictive method for identifying them in log data. The study found many factors in technology acceptance theory that were significant in terms of adoption behaviour. E-book adoption in the case study ARL was found to be related to culture of use across the dimensions of habit/automaticity, preference for online resources and platforms, and information literacy. E-book collection sufficiency, purpose or task fit, convenience, functionality, and access/copy/print/download provisions were found to be significant in terms of performance expectancy. Dimensions of effort expectancy in finding/searching/reading e-books also significantly affected user behaviour. Other significant relations comprised perceived e-book hedonic attributes (pleasantness of experience, attractiveness of formats), familiarity (awareness, prior experience, differentiability), intimacy (personal likeness, emotional attachment, preferences), facilitating conditions (such as discovery, findability, connectivity/access, courseware embedded links), moderating factors (including respondent category, student programme, age, gender, and experience/years). These factors were found to be significant as sources of gratification and continuance intention. An original contribution to knowledge was also made by deriving a predictive equation for classifying users based on transaction log data. Further, the research developed a new model of higher level information behaviours displayed by sophisticated or so-called ‘power users,’ and generated a model of e-book information behaviour maturity that distinguishes nascent from mature behaviours. The model is grounded in self-reported information behaviour. As an expansive exploration of e-book usage patterns in a case study ARL using multiple methods, the work is also innovative both in terms of scope and as an exploration of e-book adoption in an Australian context. This research is significant in laying the foundations for machine-based user profiling and enhanced individualisation of e-books to make for more satisfying user experience and acceptance of e-books