Table Of ContentLecture Notes in Social Networks
Thirimachos Bourlai
Panagiotis Karampelas
Reda Alhajj Editors
Disease Control
Through Social
Network
Surveillance
Lecture Notes in Social Networks
SeriesEditors
RedaAlhajj,UniversityofCalgary,Calgary,AB,Canada
UweGlässer,SimonFraserUniversity,Burnaby,BC,Canada
AdvisoryEditors
CharuC.Aggarwal,YorktownHeights,NY,USA
PatriciaL.Brantingham,SimonFraserUniversity,Burnaby,BC,Canada
ThiloGross,UniversityofBristol,Bristol,UK
JiaweiHan,UniversityofIllinoisatUrbana-Champaign,Urbana,IL,USA
RaúlManásevich,UniversityofChile,Santiago,Chile
AnthonyJ.Masys,UniversityofLeicester,Ottawa,ON,Canada
LectureNotesinSocialNetworks(LNSN)comprisesvolumescoveringthetheory,
foundationsand applicationsof the newemergingmultidisciplinaryfield of social
networks analysis and mining. LNSN publishes peer- reviewed works (including
monographs, edited works) in the analytical, technical as well as the organiza-
tional side of social computing, social networks, network sciences, graph theory,
sociology, semantic web, web applications and analytics, information networks,
theoretical physics, modeling, security, crisis and risk management, and other
related disciplines. The volumes are guest-edited by experts in a specific domain.
This series is indexed by DBLP. Springer and the Series Editors welcome book
ideas from authors. Potential authors who wish to submit a book proposalshould
contactAnneliesKersbergen,PublishingEditor,Springer
e-mail:[email protected]
Thirimachos Bourlai (cid:129) Panagiotis Karampelas (cid:129)
Reda Alhajj
Editors
Disease Control Through
Social Network Surveillance
Editors
ThirimachosBourlai PanagiotisKarampelas
MultispectralImageryLab—MILAB,ECE DepartmentofInformaticsandComputers
UniversityofGeorgia HellenicAirForceAcademy
Athens,GA,USA AcharnesAttica,Greece
RedaAlhajj
DepartmentofComputerScience
UniversityofCalgary
Calgary,AB,Canada
ISSN2190-5428 ISSN2190-5436 (electronic)
LectureNotesinSocialNetworks
ISBN978-3-031-07868-2 ISBN978-3-031-07869-9 (eBook)
https://doi.org/10.1007/978-3-031-07869-9
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Preface
DiseaseControl ThroughSocial Network Surveillance
Thegeneraltopicofdiseasecontrolthroughsocialnetworksurveillanceiscompli-
catedandrequiresanoptimalbalanceofingredients.Theintentionofthisbookwas
not to generate a teaching textbook and to cover all possible areas related to this
topic.Rather,weinvitedexperiencedresearcherstocoveralistofinterestingareas
of novel trends and technologies related to disease control including (1) disease
control and public health surveillance, (2) social networking surveillance and
analysis, and (3) human authentication related studies when utilizing surveillance
data and applying deep learning algorithms. These trends and technologies are
included in the ten chapters listed below that aim to provide the reader with
essential information,knowledge,and understandingon disease controlvia social
surveillance,as well as discussion on processes related to the efficient and secure
publichealthmonitoring.
ChapterContributions
In the first chapter, the authors discuss that the analysis of public perceptions
and opinion mining (OM) has received considerable attention due to the easy
availability of colossal data in the form of unstructured text generated by social
media, e-commerce portals, blogs, and other similar web resources. They choose
the Twitter platform in their research to study public perceptions regarding the
global vaccination drive. More than 112 thousand Tweets from users of different
countries around the globe are extracted based on hashtags related to the affairs
of the COVID-19 vaccine. A three-tier framework is being proposed in which
rawtweetsareextractedandcleanedfirst,visualizedandconvertedintonumerical
vectors through word embedding and N-gram models next, and finally analyzed
throughasetofmachinelearningclassifierswiththestandardperformancemetrics,
v
vi Preface
accuracy, precision, recall, and F1-measure. The authors show that the bag-of-
words(BoW) modeldevelopedachievesthe highest classification accuracy.Their
conclusions are that most of the people seem to have a neutral attitude towards
thecurrentCOVID-19vaccinationdriveandthatalsopeoplefavoringtheCOVID-
19 vaccination program are greater in number than those who doubt it and its
consequences.
In the second chapter, the authors argue that computational models for the
detection and prevention of false information spreading (popularly called fake
news) have gained a lot of attention over the last decade, with most proposed
models identifying the veracity of information. Thus, they propose a framework
basedona complementaryapproachto false informationmitigationinspiredfrom
the domain of epidemiology. In such a domain, false information is analogous
to infection, social network is analogous to population, and likelihood of people
believing an information is analogous to their vulnerability to infection. As part
of the framework, the authors propose four phases that fall in the domain of
socialnetworkanalysis.Throughexperimentsonreal-worldinformationspreading
networks on Twitter, they show the effectiveness of their proposed models and
confirm their hypothesis that spreading of false information (fake news) is more
sensitive to behavioral properties, such as trust and credibility, than spreading of
trueinformation(realnews).
In the third chapter, the authors work also on fake news mitigation strategies.
They argue that the rapid spread of fake news during the COVID-19 pandemic
has aggravated the situation and made it extremely difficult for the World Health
Organization(WHO)andgovernmentofficialstoinformpeopleonlywithaccurate
scientificfindings.Misinformationdisseminationhasbeensounhinderedthatsocial
mediasiteshadtoultimatelyconcealpostsrelatedtoCOVID-19entirelyandallow
userstoseeonlytheWHOorgovernment-approvedinformation.Thus,actionhad
to be taken because newsreaders lack the ability to efficiently discern fact from
fictionandtherebyindirectlyaidinthespreadoffakenewsbelievingittobetrue.In
theirwork,theauthorsfocusonhelpinginunderstandingthethoughtprocessofan
individualwhenreadinganewsarticle.Theyexpandthespaceofmisinformation’s
impactonusersbyconductingasetofsurveystounderstandthefactorsconsumers
deemmostimportantwhendecidingwhetherthe informationsomeonereceivesis
true or not. Experimental results show that what people perceive to be important
in deciding what is true information is different when confronted with the actual
articles.Theyconcludethatpriorbeliefsandpoliticalleaningsaffecttheabilityof
peopletodetectthelegitimacyoftheinformationreceived.
The fourth chapter discusses the topic of trends, politics, sentiments, and mis-
information during COVID-19. The authors conduct a large-scale spatiotemporal
data analytics study to understand peoples’ reactions to the COVID-19 pandemic
during its early stages. In particular, they analyze a JSON-based dataset that is
collected from news, messages, boards, and blogs in English about COVID-19
overaperiodof4months,foratotalof5.2millionposts.Thedatawerecollected
from December2019 to March 2020 from severalsocial media platformssuch as
Facebook, LinkedIn, Pinterest, StumbleUpon, and VK. The study aims mainly to
Preface vii
understandwhichimplicationsofCOVID-19haveinterestedsocialmediausersthe
most and how did they vary over time, as well as determining the spatiotemporal
distributionofmisinformation,andthepublicopiniontowardpublicfiguresduring
thepandemic.Theauthorsclaimthattheirresultscanbeusedbymanystakeholders
(e.g.,governmentofficialsandpsychologists)tomakemoreinformativedecisions,
consideringtheactualinterestsandopinionsofthepeople.
In the fifth chapter, the authors present a Data-Network Science study on a
datasetofpublicationsarchivedin“TheSemanticScholarOpenResearchCorpus”
(S2ORC) database and categorized under the area of “Disease Control through
Social Network Surveillance,” an area abbreviated from now on as “DCSNS.”
In particular, their dataset consists of 10,866 documents (articles and reviews),
retrieved through a Boolean search, published in the period from 1983, the first
year of cataloguing such publications in S2ORC, to 2020. By retrieving the
corpus of abstracts of these documents (publications) and applying the standard
LDA Topic Modeling technique, the authors claim to have found the optimal
number of six topics producing the maximum topic coherence score among the
corresponding topic models with varying numbers of topics. In that matter, the
network of their study becomes a directed citation graph of publications in the
area of DCSNS, with nodes and publications labeled by the Topics. Their aim
is to study global and local network properties with regards to clustering under
triadic relationships among connected nodes/publications,and with regardsto the
assortativityofattributesrelatedtothecontentofpublications.Theyclaimthatthey
have succeeded in analyzing the interplay between semantics and structure in the
area of publications on DCSNS, by examining and discovering the occurrence of
certain important attributes of publications in such a way that the aggregation of
publications according to these attributes is associating the meaning of attribute
affiliationstocertainstructuralpatternsofclustering,exhibitedbythebibliographic
citationnetworkofthecollectedpublications.
Thesixthchapterfocusesonprivacyinonlinesocialnetworks.Theauthorsstart
their work by arguingthat disease controlthroughonline social networks(OSNs)
hasbecomeparticularlyrelevantinthepastfewmonths.Giventhesensitivenature
ofthedatacollectedandmanipulatedinthatcontext,amajorconcernfor(potential)
usersofsuchsurveillanceapplicationsisprivacy.Theconceptofprivacyhasbeen
studiedfrommanydifferentangles,andthisworkaimstoofferageneralsystematic
literature review of the area. The contributionsof their book chapter are twofold.
Firstly,theyproposeasystematicmappingstudycoveringpapersrelatedtoprivacy
inOSNs.Thisstudyresultsinacoarse-grainedoverviewofthelandscapeofexisting
worksinthefield.Inthisfirstphase,345paperswereexamined.Thefindingsshow
the characteristicsandtrendsof publicationsin the area. Theyalso emphasizethe
areas where there is a shortage of publications, hence guidingresearchersto gaps
in the literature. Secondly, they propose a classification framework and apply it
to the subset of 108 papers that offer a solution to protect the user’s privacy.The
resultsprovideawayforresearcherstopositionasolutionincomparisonwithother
existing solutions and they also highlight trends in existing solutions. The main
viii Preface
practical implications of this book chapter are guidelines and recommendations
proposedtodesignersofapplications,includingapplicationsfordiseasecontrol.
Intheseventhchapter,theauthorsarguethatthehealthcrisisbroughtaboutby
COVID-19hasresultedinaheightenednecessityforproperandcorrectinformation
dissemination to counter the prevalence of fake news and other misinformation.
Doctors are the most reliable source of information regarding patients’ health
status, disease, treatment options, or necessary lifestyle changes. Prior research
has tackled the problem of influence maximization (IM), which tries to identify
the most influential physicians inside a physician’s social network. However, less
research has taken place on solving the problem of volume maximization (VM),
which deals with finding the best set of physicians that maximize the combined
volume(e.g.,medicineprescribed)andinfluence(i.e.,informationdisseminated).In
thischapter,theprimaryobjectiveoftheauthors’workistoaddresstheVMproblem
byproposingdifferentalgorithmicframeworks,includingareinforcementlearning
(RL) one. The authors compared the frameworks while using the physicianSN
dataset(physiciansocialnetwork181nodesand19,026edges)andtesteddifferent
algorithms. Their research highlights the utility of using reinforcement learning
algorithms for finding critical physicians that can swiftly disseminate critical
informationtobothphysiciansandpatients.
Intheeighthchapter,theauthorsfocusonastudythatdiscussestheperceptionof
IndianpopulationonCOVID-19vaccineshortageduringtheperiodofarapidhike
ofcasesintheCOVID-19secondwave.UsingaTwitterAPI,46,000uniquetweets
of Indian citizens have been scrapped, which include the following key words,
namely“vaccine,”“secondwave,”and“COVID.”Theauthorsusedatopicmodel.
Inmachinelearningandtweetprocessing,atopicmodelisatypeofstatisticalmodel
fordiscoveringallabstracttopics,whichhappentoappearinacollectionoftweets
(in this study of tweets relevantto the secondwave of COVID). In practice, topic
modeling is a text-mining tool that is occasionally used in tweets for the purpose
of discovering hidden semantic structures in the tweet text body. In this work, it
was used to analyze a set of key themes based on the perception of people. The
study shows thatthe Indianpopulationis concernedaboutvaccine shortage and a
collective effortis recommendedto be followedfor the improvementof the well-
beingoftheIndiancitizens.
In the ninth chapter, the authors work on a case study relevant to a pandemic
era. They aim to address the problem of face detection in the MWIR (thermal)
spectrumwhenthefacesareoccludedwithfacemasks.Sincethepubliclyavailable
datasets are not large enough to train original models, transfer learning is used
on models trained on the COCO dataset. The models are first trained and tested
on masked face images, which results in high precision and recall values for all
models. Then, these models are tested on masked face images, and the precision
and recall metrics drop significantly. Performance drops also when the models
proposedaretestedonmaskeddatawithamarginalbutnoteworthyincreaseofthe
inference time. Then, the proposed models are trained and tested on masked face
data, and they yield an 89.4% precision and 92.1% recall rates, respectively. The
improvedfacerecognitionresultswhenusinganefficientautomatedfacedetection
Preface ix
approachfurtherdemonstratetheimportanceofsuchmodelsoperatingintheMWIR
spectrum.Thisworkisproposedtobefurtherextendedtoscenarioswherethedata
usedarecollectedinthevisiblespectrumandunderconstrainedconditions,aswell
as to scenarios where the data used are collected in both the thermal and visible
bands and under outdoor, unconstrained settings. The study concludes that with
sufficientrealdata,efficientunifiedmodelsforeachbandthatdetectshumanfaces
ateachdistancecanbedeveloped.
The tenth chapter investigates the problem of face mask compliance classifi-
cation in response to the coronavirus disease (COVID-19) pandemic. Since the
start of the pandemic, many governments and businesses have been continually
updating policies to help slow the spread of the virus, including requiring face
masks to use many public and private services. In response to these policies,
many researchers have developed new face detection and recognition techniques
formaskedfaces,almostexclusivelyfocusingondetectingthepresenceorabsence
of someone wearing a face mask or not. In this work, the authors investigate the
capability of modern classification algorithms to efficiently distinguish between
maskedfaceimages,capturedinthevisibleandthermalbands,andwhichwornin
complianceornotwiththesuggestedguidelinesprovidedbyhealthorganizations.
The approach proposed is deep learning (DL) based and is composed of the
creationofamulti-spectralmaskedfacedatabasefromsubjectswearingfacemasks
or not; then, it continues with the augmentation of the generated database with
synthetic face masks to simulate two different levels of non-compliant wearing
of face masks; and finally, it assesses a variety of DL-based architectures, on the
previous augmented database, to investigate the efficiency of different classifiers
on face mask compliance when operating in either the visible or thermal bands.
Experimentalresultsshowthatfacemaskcomplianceclassificationinbothstudied
bandsyieldsa classification accuracythatreaches100%formost modelsstudied,
when experimenting on frontal face images captured at short distances and with
adequateillumination.
The eleventh and final chapter discusses the effectiveness of periocular-based
human authentication algorithms when wearing face masks that aim to slow the
spread of viruses, such as the COVID-19. According to a study published by the
National Institute of Standards and Technology (NISTIR 8311), the accuracy of
facial recognition algorithmsis reduced between 5% and 50% when comparedto
the accuracy yielded by the same algorithms when the subjects are not wearing
face masks. The same report also states that face images of subjects wearing
masks can increase the failure to enroll rate (FER) more frequently than before.
In addition, it is discussed that masked face images lower the efficiency of
surveillance (unconstrained) face recognition systems, which become even more
pronetoerrorduetoocclusion,distance,cameraquality,outdoors,andlowlight.In
this book chapter, the authors focus on the effectiveness of dual-eye periocular-
based recognition algorithms when the subjects are wearing face masks under
controlled and challenging conditions, and when the face images are captured in
both the visible and MWIR (mid-wave infrared) bands. The authors first utilize
MILAB-VTF(B),achallengingmulti-spectralfacedatasetcomposedofthermaland
