ABDOLLAHYAN, M

Applications of Intelligent Systems

BESSANT, C

Mondragón, RJ

SMERALDI, F

Queen Mary Research Online

April 2018Visualising the Topological Structure ofHealth-related Message Board UserNetworksMaryam Abdollahyan a,1, Rau´l J. Mondrago´n a, Conrad Bessant b andFabrizio Smeraldi aaSchool of Electronic Engineering and Computer Science, Queen Mary University ofLondon, United kingdombSchool of Biological and Chemical Sciences, Queen Mary University of London,United KingdomAbstract. Health-related message boards are common digital health platforms withgreat practical and sociological relevance. In this work, we present an investigationof the topological structure of interactions in such forums. We visualise the interac-tions among users and between users and forum administrators using a simple yeteffective network visualisation tool called the Bitmap of Sorted Adjacency Matrix(BOSAM) algorithm. We apply the BOSAM algorithm to six health-related mes-sage boards of various scopes and sizes. The outcome reveals major differencesbetween the user interaction networks of these forums. The results of our analysiscorrelate closely with the characteristics of the respective message boards, includ-ing their topic coverage, presence of user communities, commercial or communitynature and management style.Keywords. network models, digital health, visualisation, BOSAM1. IntroductionHealth-related message boards are an important type of digital health platform. Theseforums, over the years, have evolved from mainly informal support groups to more sys-tematic efforts. These include commercially run forums that support official patient as-sociations, facilitate patient access to medical professionals or aim at gathering infor-mation for the benefit of pharmacological and medical research. This has attracted theattention of sociologists, policymakers and computational scientists, with various studiesaimed at modelling the evolution of forums [5], characterising the participation styles ofpatients [2] or optimising patient information extraction [8].The analysis of the structure of health-related message boards has implications forboth forum managers and users. Measuring the engagement and responsiveness levels ofuser interactions in a forum allows its managers to assess the capacity of their forum andsubsequently adjust the level of support in the forum. In addition, it helps patients decide1Corresponding Author: Maryam Abdollahyan, Queen Mary University of London, Mile End Road, LondonE1 4NS, United Kingdom; E-mail: m.abdollahyan@qmul.ac.ukApril 2018which forums to join. Understanding how users contribute to a forum assists its owners indeveloping strategies that would promote growth within the forum and facilitate accessto the information on the forum [4,10].In this work, we take a wider-angle view of health-related message boards as socialnetworks, focusing on the topology of interactions among the users and between theusers and forum administrators. We use a network visualisation tool called the Bitmapof Sorted Adjacency Matrix (BOSAM) algorithm [6] to represent the structure of theseinteractions. Many studies require a detailed analysis of user posting behaviour, possiblythe semantic annotation of their posts, the information provided by users in their publicprofiles (e.g. age and gender) or metrics that are only accessible by forum administrators(e.g. total time users are logged in) [1,7]. In contrast, we employ a simple metric thatis easily obtainable for any forum. Namely, we define the interactions between users asposting to the same conversation thread. Results of applying our approach to six differenthealth-related message boards indicate that the resulting bitmaps reflect the nature of theforums, in terms of the topics being discussed, participation of their users in communitiesand their administration style.2. Overview of MethodThe connectivity of a network is characterised by its adjacency matrix. Given an undi-rected network containing N nodes with indices 1,2, ...,N, its adjacency matrix A is de-fined as an N×N matrix where ai j is 1 if there is a link connecting nodes i and j, and 0otherwise. Matrix A can be represented as black and white bitmap image where the pixelat a coordinate (i, j) is black if ai j = 1 and white otherwise. This binary representationgives an overall impression of the network connectivity, however, in general it looks likea collection of random points when the indices of nodes are arbitrary. For this represen-tation to reflect the topological properties of the network, the nodes can be sorted so thattheir indices correspond to a measure of their connectivity [3,9]. One such representationis the Bitmap of Sorted Adjacency Matrix (BOSAM) [6]. The BOSAM of a network isobtained as follows.Specifically, the nodes are reindexed by sorting them in the ascending order of theirdegree (i.e. the number of links they have). If two nodes have the same degree, thenthey are ranked in the ascending order of the largest degree of their neighbours. In thestandard algorithm, remaining ties are broken by reordering the nodes in the ascendingorder of the largest index of their neighbours. Here, we use a slightly different approach:in the case of ties, we reorder the nodes in the ascending order of the largest degree oftheir second neighbours and so on. The bitmap visualisation of the reordered adjacencymatrix is the BOSAM of the network.The BOSAM often resembles a “flower”. The shape of the components of this floweris related to the topological properties of the network. In Section 4, we discuss some ofthese properties in the context of message board user interaction networks. If a networkpreserves its macroscopic structure while it grows, then the shape of the components ofits BOSAM will scale with respect to the network’s size. Hence, looking at the BOSAMsfor two networks (which can be of different sizes), one can tell if they have the samemacroscopic structure.April 20183. DatasetsWe collected data from six health-related message boards (see Table 1). Among these,three (CROHN, HDA and PSOR) are focused on a specific disease, while the others(HEALTH, INSPIRE, PINFO) cover various health topics. Moreover, the CROHN, HDAand PSOR message boards appear to be community efforts, while the others are managedin a more “commercial” manner. To model the user interaction network for each messageboard, we created a node for each user and linked two nodes if their corresponding usershad posted to the same conversation thread.Table 1. Names of the message boards (networks) and number of users (nodes) and posts we processedMessage Board1 Number of Users Number of PostsCrohn’s Disease Forum (CROHN) 21703 673162Health Boards (HEALTH) 403682 4929836Huntington’s Disease Association (HDA) 1245 34173Inspire communities (INSPIRE) 221432 3725555Patient Info (PINFO) 139567 1699131Psoriasis Association (PSOR) 1816 44024. Results and DiscussionFigure 1 shows the BOSAMs for the networks we modelled. In the HDA network, con-nectivity is dominated by the nodes with higher degrees as pixels are distributed denselyalong the upper right corner of its BOSAM, meaning that users of low degree tend tointeract with users of high degree and vice versa. However, no other obvious structure isobserved. Hence, the HDA message board appears as a single-topic forum that patientsuse to ask questions from expert users. In contrast, in the PSOR network, users tend tointeract with other users of similar degree. This is indicated by the ‘squares’ near thediagonal of its BOSAM which show the formation of communities of nodes with similardegree, with noticeably larger communities consisting of nodes with high degree that aretightly linked together. Furthermore, these node communities observed in the BOSAMcorrespond to the patient communities on the PSOR forum, where patients in each com-munity have been diagnosed with a different type of psoriasis. The CROHN network,also a single-topic forum being run by the community, exhibits a mixture of the abovetwo characteristics.The HEALTH, PINFO and INSPIRE message boards, being large commerciallyrun forums, have much richer structures. Their BOSAMs resemble a flower where eachcomponent ‘leaf’ has a statistical meaning. At a closer look, each leaf has a step-likeshape (see Figure 2). The length of the leaf’s base is equal to the number of nodes withdegree k given by N∑k+1i=k P(i), where P(k) is the probability of finding a node with degreek in the network. The height of the steps is equal to the number of nodes with degreek′, again given by N∑k′+1i=k′ P(i). The tread of the k′-th step is equal to the number of1CROHN: www.crohnsforum.com; HEALTH: www.healthboards.com; HDA: www.hdmessageboard.com;INSPIRE: www.inspire.com; PI: patient.info; PSOR: www.psoriasis-association.org.ukApril 2018(a) HDA (b) PSOR(c) CROHN (d) HEALTH(e) PINFO (f) INSPIREFigure 1. BOSAMs for the modelled networks. Node degree increases along the axes.April 2018= N∑k′+1k′ P(k)N∑k+1k P(k)correlationsFigure 2. Characteristics of the BOSAM componentslinks that have degree k at one end and degree less than k′ at the other end. The den-sity of points inside a component is related to the conditional probability P(k′|k). The‘lines’ radiating from the upper right corner of the bitmap are a feature specific to so-cial networks, and represent correlations. An example of a three point correlation isshown in Figure 2, where the blue, green and red circles have coordinates (x,y), (y,z) and(z,x) that fall on these lines; such points represent a triple of mutually interacting users.The BOSAMs for the HEALTH and INSPIRE networks exhibit regions of denseshading which are likely related to posts by the site administrators. This is notable sinceboth of these forums are multi-topic and therefore would normally divide into communi-ties with little interaction between them. However, particularly in the case of INSPIRE,site administrators routinely cross-post across the forum and also, to some extent, allowcross-posting by users in the form of journals. This feature tends to obliterate the commu-nities structure. Parts of that structure can still be seen in the BOSAM for the PINFO mes-sage board, which suggests that, in comparison, this forum is more lightly managed by itsadministrators.5. ConclusionsWe presented an analysis of large samples of user interactions from a number ofhealth-related message boards using the BOSAM algorithm. Results based on a sim-ple measure of interaction (i.e. whether two users contribute to the same conversationthread) show an interesting variety of structures that appear to correlate with the fea-tures of the corresponding forums such as the topic coverage of the forum (single-topic or multi-topic), the presence of user communities, the commercial or communitynature of the forum and consequently its management style. In future work, we planto include other measures of interaction in our analysis that take into account, for in-stance, the frequency of interactions and the structure of replies within each thread.References[1] E. Børøsund, M. Cvancarova, M. 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Modeling the temporal evolution of an online cancer forum.In Proceedings of the 1st ACM International Health Informatics Symposium, pages 356–365. ACM,2010.[6] Y. Guo, C. Chen, and S. Zhou. Topology visualisation tool for large-scale communications networks.Electronics Letters, 43(10):597–598, 2007.[7] R. Jones, S. Sharkey, J. Smithson, T. Ford, T. Emmens, E. Hewis, B. Sheaves, and C. Owens. Usingmetrics to describe the participative stances of members within discussion forums. Journal of MedicalInternet Research, 13(1), 2011.[8] Y. Liu and Y. Chen. Patient-centered information extraction for effective search on healthcare forum. InSBP, pages 175–183. Springer, 2013.[9] R. J. Mondrago´n. Topological modelling of large networks. Philosophical Transactions of the RoyalSociety of London A: Mathematical, Physical and Engineering Sciences, 366(1872):1931–1940, 2008.[10] X. Song, S. Jiang, X. Yan, and H. Chen. 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Visualising the Topological Structure of Health-related Message Board User Networks

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Visualising the Topological Structure of Health-related Message Board User Networks

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