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doi:10.1002/asi.v60:5

Cited by 100 publications

(9 citation statements)

References 579 publications

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“…We further construct a social network to characterize individuals' interaction relationships based on the data of a Facebook-like online community [54], in which the registered users can communicate with each other online through personal blogs and forum postings. In this network, there are in total 1899 nodes and 13 838 undirected links among them.…”

Section: Basic Scenariomentioning

confidence: 99%

A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions

Shang

Liu

2014

J. R. Soc. Interface.

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During an epidemic, individuals' decisions on whether or not to take vaccine may affect the dynamics of disease spread and, therefore, the effectiveness of disease control. Empirical studies have shown that such decisions can be subjected to individuals' awareness about disease and vaccine, such as their perceived disease severity and vaccine safety. The aim of this paper is to gain a better understanding of individuals' vaccination behaviour by modelling the spread of awareness in a group of socially connected individuals and examining the associated impacts on their vaccination decision-making. In our model, we examine whether or not individuals will get vaccinated as well as when they would. In doing so, we consider three possible decisions from an individual, i.e. to accept, to reject, and yet to decide, and further associate them with a set of belief values. Next, we extend the Dempster–Shafer theory to characterize individuals' belief value updates and their decision-making, having incorporated the awareness obtained from their connected neighbours. Furthermore, we examine two factors that will affect individuals' vaccination decisions: (i) reporting rates of disease- and vaccine-related events, and (ii) fading coefficient of awareness spread. By doing so, we can assess the impacts of awareness spread by evaluating the vaccination dynamics in terms of the number of vaccinated individuals. The results have demonstrated that the former influences the ratio of vaccinated individuals, whereas the latter affects the time when individuals decide to take vaccine.

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Section: Basic Scenariomentioning

confidence: 99%

A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions

Shang

Liu

2014

J. R. Soc. Interface.

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“…Raittila, 1996;Raittila ym., 2009Raittila ym., , 2008. Informaatiotutkimuksen alalta tämäntyyppistä tutkimusta ei ole tehty lukuun ottamatta parisuhdeväkivaltaa (Westbrook, 2008(Westbrook, , 2009) ja luonnonkatastrofeja (Bruns & Burgess, 2014;Choo, 2009;Choo & Nadarajah, 2014;Griffin ym., 2008;Lopatovska & Smiley, 2014;Pang, 2014) koskevaa informaatiokäytäntöihin kohdistunutta tutkimusta. Sen sijaan terrori-iskujen ja muiden kansalaisia laajemmin koskettavien kriisitilanteiden tiedontarpeita ei tietääksemme ole analysoitu edellä mainittua Boylen ja kumppaneiden (2004) artikkelia lukuun ottamatta.…”

Section: Aiempi Tutkimusunclassified

“…Choon ja Nadarajahin (2014) Australian metsäpaloja käsittelevässä tutkimuksessa huomio on keskittynyt metsäpaloja koskevaan vaaratiedotukseen ja siihen liittyviin tiedontarpeisiin ja tiedonlähteisiin ennen varsinaisen palon leviämistä alueelle. Myös Choo (2009) tarkastelee luonnonkatastrofeihin liittyvien varoitusjärjestelmien merkitystä.…”

Section: Aiempi Tutkimusunclassified

Terrori-isku tiedontarpeiden virittäjänä: Turun puukotusten aiheuttamat tiedontarpeet

Haasio

Mattila

Ojaranta

et al. 2018

INF

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In this study, we have analysed what kind of information needs arise in case of a sudden random act of violence and which factors trigger these information needs. The case represented in this study is the stabbing in Turku which took place in August 18th, 2017, which led to the death of two people. The data in this study consists of the Facebook comments collected from the media company Yle News' Facebook-page. The first 13 posted news and their comments were collected during the first eight hours after posting the news. The total data consists of 1930 comments and 212 recognized information needs in their content. Quantitative and qualitative analysis were used for analysing the data. The study shows that, during crisis, information needs change as more information is released. Most information needs were directed to the media and especially the activities of media company Yle. Other frequent information needs included questions about the offender and issues concerning immigration politics. In the study, information needs were categorized into 15 different types that can be divided in to four main groups: authority related, act related, world view related and other information needs.Asiasanat: tiedontarve; tiedonhankinta; terrorismi; sosiaalinen media Artikkeli on lisensoitu Creative Commons Nimeä-EiKaupallinen-JaaSamoin 4.0 Kansainvälinen -lisenssillä

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“…Panzarasa et al [Panzarasa et al 2009] used an online community platform provided for students of the University of California, Irvine, to assess how the social network structure of the community platform evolved over time. The authors found that certain users acted consistently as hubs through which communication was mediated.…”

Section: Social Network Evolutionmentioning

confidence: 99%

Mining User Development Signals for Online Community Churner Detection

Rowe

2016

ACM Trans. Knowl. Discov. Data

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Churners are users who stop using a given service after previously signing up. In the domain of telecommunications and video games, churners represent a loss of revenue as a user leaving indicates that they will no longer pay for the service. In the context of online community platforms (e.g. community message boards, social networking sites, question-answering systems, etc.) the churning of a user can represent different kinds of loss: of social capital, of expertise, or of a vibrant individual who is a mediator for interaction and communication. Detecting which users are likely to churn from online communities therefore enables community managers to offer incentives to entice those users back; as retention is less expensive than resigning users up. In this paper we tackle the task of detecting churners on four online community platforms by mining user development signals. These signals explain how users have evolved along different dimensions (i.e. social and lexical) relative to their prior behaviour and the community in which they have interacted. We present a linear model, based upon elastic-net regularisation, that uses extracted features from the signals to detect churners. Our evaluation of this model against several state of the art baselines, including our own prior work, empirically demonstrates the superior performance that this approach achieves for several experimental settings. This paper presents a novel approach to churn prediction that takes a different route from existing approaches that are based on measuring static social network properties of users (e.g. centrality, in-degree, etc.).

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Untitled

Cited by 100 publications

References 579 publications

A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions

A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions

Terrori-isku tiedontarpeiden virittäjänä: Turun puukotusten aiheuttamat tiedontarpeet

Mining User Development Signals for Online Community Churner Detection

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