e-Health for Older Adults: Navigating Misinformation

Amira Ghenai

1,2

, Xueguang Ma

, Robin Cohen

, Karyn Moffatt

, Andy Yang

and Yipeng Ji

David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada

School of Information Studies, McGill University, Montreal, Canada

Keywords:

Position Paper, Online Misinformation, e-Health, Older Adults, Social Media, Websites.

Abstract:

In this position paper, we advocate for the design of more progressive online social networks and their web

pages for the user base of older adults, arguing that in order to address the issue of misinformation, strategies

attuned to this population of users in particular are needed. We discuss challenges that arise with misleading

health information and with websites that support questionable positions with fake reviews (often generated

by bots). We also discuss the contribution of search engine results to the difﬁculties that this population faces

when navigating misinformation. We propose an approach where more interaction with users is promoted,

and where education about the perils of the online world can be supported, as an additional tool for reducing

misinterpretations which may lead to signiﬁcant negative outcomes. The algorithms which we propose come

from the computer science subﬁelds of artiﬁcial intelligence, human-computer interaction and information

retrieval. The novel stance is insisting on solutions that ﬁt the demographic in question especially well, instead

of relying on one-size-ﬁts all approaches, which may disadvantage users who are older adults.

1 INTRODUCTION

In this position paper, we introduce some new direc-

tions for a series of concerns regarding e-health and

older adults, for the speciﬁc challenge of assisting

with online misinformation. We ﬁrst consider mis-

information as it arises in social networks, examin-

ing both how older adults may contribute to providing

misleading content, and also why this base of users

requires special consideration when developing solu-

tions for provide assistance in coping with challeng-

ing content. With this important background in hand,

we then comment on the speciﬁc case of social me-

dia posts regarding healthcare, providing insights into

why this context in particular requires special con-

sideration when considering older adult users. Our

next subtopic explores fake reviews on health-related

products on websites and discusses why older adults

in particular may need speciﬁc assistance. We ﬁrst

discuss a collection of reviews about healthcare, dis-

covered online in April 2020, identifying the char-

acteristics that strongly suggest questionable content.

Based on research regarding older adult attitudes to-

wards online information in general, we then make

some speciﬁc recommendations, highlighted in ital-

ics. We also advocate creating automated solutions

for improving online experiences, so that possible

harm from misinformation is mitigated. An impor-

tant thread for our discussion is exploring current

approaches for detecting whether online reviews are

fake, using machine learning. We point the reader to

a later subsection of the paper where we discuss fur-

ther how to attune these solutions to older adults.

A third contributor to misleading content online is

the output of search engines. We discuss concerns that

arise due to these browsing efforts, reﬂecting as well

on what may be most relevant for older adults, specif-

ically when the topics at hand are those of healthcare.

The strategy that we propose for providing better as-

sistance to this user base for this particular challenge

is to conduct detailed user studies in order to carefully

learn what poses the most difﬁculty. The engagement

of participants yields further insights into the speciﬁc

problems to address with online misinformation and

the strategies that may hold the most promise in order

to assist users in this demographic.

We move forward from here to a section of the pa-

per devoted to examining the use of techniques from

artiﬁcial intelligence multiagent trust modeling, out-

lining their promise both in assisting with detection of

misinformation and in delivering help to older adults

in particular. The methods which we outline enable

reasoning both about the speciﬁcs of the user and the

particular content at hand, in order to propose strate-

gies for improving online experiences. We preface

Ghenai, A., Ma, X., Cohen, R., Moffatt, K., Yang, A. and Ji, Y.

e-Health for Older Adults: Navigating Misinformation.

DOI: 10.5220/0010463001950202

In Proceedings of the 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2021), pages 195-202

ISBN: 978-989-758-506-7

195

this discussion with some insights into the user base

of older adults and what may be most important for

them when addressing untrustworthy health informa-

tion. As part of our ﬁnal reﬂection for the future, we

consider more concrete steps forward using computer

science methods to improve the state of the world. We

also highlight the treatment of health information and

raise the current concern of Covid. In our conclusion,

we advocate for a combination of intelligent process-

ing of online information and a strategy of better ed-

ucation, going forward. It is important to note that

each online user is an individual; we are not claiming

that all older adults will ﬁt the same proﬁle. All the

same, certain challenges that have been shown to be

more prominent for this user base merit consideration.

And while older adult users may enjoy distinct bene-

ﬁts online due to the wisdom of their age, since failing

to address healthcare misinformation may have criti-

cal outcomes, we choose to focus on steps to deﬂect

concerns.

2 THE CENTRAL CONCERNS

FOR OLDER ADULTS WITH

ONLINE MISINFORMATION

2.1 Social Networking

When considering misinformation in online social

networks and the speciﬁc concern about misleading

healthcare posts, research shows that this may be

more prevalent in certain platforms. For example,

Ebola misinformation was present in more than 80%

of Instagram posts but fewer than 50% of Tweets

(Guidry et al., 2017). There is also an issue with the

spread of rumors and how this changes over time. As

an example, reports on narcolepsy as a side effect of a

ﬂu vaccine in the Nordic countries were ﬁrst depicted

as rumors, but later few cases were conﬁrmed after

years of research and this is still contested (Sturken-

boom, 2015). Acting on health misinformation might

also need urgent actions from health organizations to

stop the spread and share correct information. Detect-

ing health rumors in a timely fashion can help public

health ofﬁcials tackle them before they spread. In the

case of the Ebola outbreak, some of the rumors cir-

culating the Internet, such as that drinking salty wa-

ter was an effective protective measure, led to several

deaths (Oyeyemi et al., 2014).

One promising approach is focusing on this im-

portant issue of rumor spread in social media, propos-

ing methods for detecting the origins of misinforma-

tion using a combination of crowdsourcing and expert

advice, examining features of the messages. Algo-

rithms to assist in detecting misinformation and ru-

mour spread have been designed speciﬁcally for con-

cerns with health within Twitter (Ghenai and Mejova,

2017). While this research also examined speciﬁc

health issues (e.g. Zika fever, cancer), a range of pos-

sible health-related topics could also be considered by

the methods presented in this paper.

Since some research has shown that older adults

may actually quite beneﬁt from using social media

(Myhre et al., 2016) in terms of improved working

memory and cognitive function, it would be valuable

to encourage these users to participate in online com-

munities. But two problems arise. The ﬁrst is that

older adults may be responsible for the spread of mis-

information. For example, in a study of registered

voters on Twitter (admittedly only a small sample of

the older adult users), Grinberg et al. (Grinberg et al.,

2019) found that people over 65 shared roughly 4.5

as many false news stories on Twitter as people 18

to 24. Older adults were also found to have shared

more fake news and hoaxes in Facebook (Guess et al.,

2019), nearly 7 times as many articles from fake news

domains than younger users, and also 3 or 4 times as

many fake news links. The second problem is that

older adults are more susceptible to misinformation,

compared to younger adults (Wylie et al., 2014). As

to why these users may be more prone to believe in-

correct information, studies such as (Ghosh and Iyen-

gar, 2019) suggest that feelings of isolation and lone-

liness may be important factors in willingness to be-

lieve the misinformation. This research suggests that

more speciﬁc attention on the user base of older adults

is merited, when trying to address online misinfor-

mation. The importance of doing so becomes all

the more essential when considering healthcare posts

with fake reviews, as we discuss in the section below.

2.2 Fake Reviews

A recent study (Shao et al., 2019) suggests that

credulity is the factor that increases vulnerability

to fraud in older adults where credulity refers to a

propensity to believe things that are unproven or un-

likely to be true. Slower cognitive processing and in-

creased trust might also be reasons why fraud among

older adults is efﬁcient (Bailey and Leon, 2019). Liao

et.al (Liao and Fu, 2014) conducted a study to un-

derstand how age affects credibility judgements of

online health information. The results showed that

older adults pay less attention to credibility content

cues (e.g. high ranked medicine with research evi-

dence, explanation of treating mechanisms, compre-

hensive and accurate information, professional writ-

ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health

196

ing) compared to younger adults. Different from

younger adults, older adults further tend to ignore the

contextual website features during credibility judg-

ment (such as nice layout/color/information struc-

ture, with reference/contact information/third party

endorsement ). When looking at user reviews, older

adults were helped in making better credibility judg-

ments when these reviews were consistent with con-

tent cues. We are encouraged as well by the work of

(Stewart et al., 2014) that suggests how credibility in-

dicators can be promoted for older adults.

From this study, we conclude that older adults

might beneﬁt from explicit, prominent guidelines to

better perform credibility judgements. On the de-

sign perspective, website designers might use pop-up

checklist that “reminds” users to ask critical ques-

tions relevant to the credibility of information and to

pay attention to Web-speciﬁc features, which may help

older adults to naturally adopt more effective credibil-

ity judgment when consuming online health informa-

tion.

2.2.1 Older Adults and Healthcare

We discovered several examples of reviews of health-

care products on Amazon in April 2020 which we sus-

pected were fake. The ﬁrst was for an essential min-

eral supplement “Pure” which has few words (“hair

grew back”), and claims huge beneﬁt (“bald cure”).

At times we saw review text with strong titles (“Five

stars” or “Great product”) that was incoherent, pos-

sibly autogenerated, including “I gave this to my son

since I have the mugs that are copper he likes it” and

“This is the very best Melatonin in the market to-

day. Buy up while you can” (where the product was

not even melatonin-based). Generic wording at times

seemed canned, as well. This happened with a re-

view for a ﬁngertip pulse oximeter which claimed “I

need this instrument very much to detect the physical

condition of me and my family”. Stilted writing and

fearmongering were also elements which heightened

our concern about the reviews. This study of possi-

ble fake reviews points to the value of algorithms (ex-

plored in the next subsection) which delve further into

the linguistic style of reviews and which may be able

to detect irrelevant content.

von Helversen et al. (von Helversen et al., 2018)

considered the differences in how younger and older

adults assessed products based on three attributes:

product attributes, average ratings, affect-rich (emo-

tional) user reviews. In the absence of any ratings or

reviews, they found that older adults were more likely

to carefully consider the product attributes. How-

ever, they still struggled to determine the better prod-

uct just by looking at the attributes, which may be

affected by aging-related declines in their decision-

making abilities. They also found that younger adults

were strongly inﬂuenced by average consumer rat-

ings and in the absence of user reviews would most

likely choose the product with a higher average rat-

ing unless there was jarring difference between the at-

tributes of the product. In comparison, average ratings

had no such effect on the decisions by older adults,

which they found to be surprising. They postulate that

it’s possible that older adults simply don’t care about

what other people think or that the user interface for

average ratings may be difﬁcult for older adults to un-

derstand or see. As for user reviews, they found that

younger adults were strongly affected by both affect-

rich negative reviews and affect-rich positive reviews.

In contrast, older adults were not at all affected by

affect-rich positive reviews but strongly affected by

affect-rich negative reviews. They claimed that this is

consistent with the socio-emotional selectivity theory

since older adults want to focus on emotional goals

and thus want to avoid negative emotions that may be

associated with choosing a product.

Below we provide some of our key proposals in

italics. The researchers conclude and we recommend

that rating systems be adapted for older adults by

having better visibility of all negative reviews and

more clarity regarding average ratings. It is of course

also important to prevent the injection of fake, vivid,

negative reviews. The researchers also observed and

we ﬁnd it important to emphasize that the next gen-

eration of older adults may be more internet-savvy.

It is also important to note that the authors’ base-

line reviews were skewed towards positive statements

since they were more typical than neutral reviews. We

therefore advocate that another study should examine

whether these results are repeatable. If they are, then

platform owners such as Amazon should make use

of content moderation to be more circumspect with

strongly positive reviews that may be fake. However,

freedom of speech still needs to be balanced; users

should not feel that there is undue censorship.

2.2.2 Automating the Assistance

It is important to note that fake reviews arise online

in part because they are generated by automated pro-

cedures, commonly referred to as bots. For example,

this signiﬁcant study examined how artiﬁcial intelli-

gence technology could be used to populate websites

with fake reviews (Yao et al., 2017). This work then

examines how to address this practice using technol-

ogy. In fact, several researchers have designed ma-

chine learning algorithms to detect fake reviews and

we suggest also using these methods as part of the ef-

fort to assist older adults.

e-Health for Older Adults: Navigating Misinformation

197

The work of Ott et al. (Ott et al., 2011) exam-

ines linguistically analyzing the content of reviews

online in order to identify those which may be sus-

pect. Speciﬁcally, their ﬁndings suggest the im-

portance of considering both the context (e.g., BI-

GRAMS+) and motivations underlying a deception,

rather than strictly adhering to a universal set of de-

ception cues. Several other approaches for detect-

ing fake reviews have been developed over the years.

The performance of different methods is examined in

ajek et al., 2020). The work of Sun et al. (Sun

et al., 2013) makes us aware that various spamming

techniques can still be used to thwart fake news de-

tection algorithms. And the research of Mukherjee et

al. (Mukherjee et al., 2013b) critiques just how ef-

fective methods being used today by some of the cur-

rent platform owners turn out to be, examining what

is happening in the context of Yelp.

We also assembled a set of papers aimed at de-

tecting fake reviews which introduce deeper AI tech-

niques for the analysis. The techniques spanned su-

pervised learning (supporing binary classiﬁcation),

unsupervised learning (to address lack of labelled

data) and semi-supervised learning; both text and be-

haviour features were considered. The approach in

(Mukherjee et al., 2013a) formulates detection as an

unsupervised clustering problem based on user be-

haviours, using Bayesian methods. The work of

(Li et al., 2017) shows the AI methods of hidden

Markov Models, exploiting bimodal distributions and

behaviour features. Some of the methodology used in

linguistic processing of text is revealed in (Shuqin and

Jing, 2019), following a semi-supervised learning ap-

proach. Examining the contribution of temporal fea-

tures is the core idea of the next paper examined (Liu

et al., 2019), as a speciﬁc element of interest. We also

analyzed a supervised learning proposal that focuses

on text features, culminating in a machine classiﬁca-

tion process (Jia et al., 2018). A useful literature re-

view in the area is offered in (Wu et al., 2020); this pa-

per makes clear that effective methodology and useful

datasets are both important.

Some of the central issues that need to be ad-

dressed are as follows. First of all, obtaining ground

truth data is a challenge. As explained above, one ap-

proach is to use crowdsourced labelling towards this

end, but the accuracy of these methods to date for

fake reviews has faced various challenges (Fornaciari

et al., 2020). With respect to feature detection, our ob-

servation is that using behaviour features for fake re-

views needs to focus on the review account behaviour,

such as how many reviews were posted per day. This

is distinct from user behaviour analyses that arise in

studies such as (Ghenai and Mejova, 2017). We ex-

plore how some of this feature analysis can be tuned

to older adults in particular, in Section 3.

2.3 Older Adults and Search Engines

Even if search engines return the most relevant doc-

uments, sometimes these relevant documents contain

incorrect information. The presence of incorrect in-

formation might cause harm when making health-

related decisions. In a prior study (Ghenai et al.,

2020), the authors showed that there is a signiﬁ-

cant inﬂuence of search results on people’s decisions

when determining the efﬁcacy of a medical treat-

ment. When search results were biased towards cor-

rect information, people tended to answer health re-

lated questions with high accuracy. On the other hand,

when the search results were biased towards incorrect

information, people’s accuracy dropped. The conse-

quences of the presence of incorrect information in

search results are dire especially when dealing with

serious illnesses. For example, a search user is diag-

nosed with cancer, and turning to a web search en-

gine, ﬁnds documents offering many treatments for

sale, but many of these treatments will have no effect

or even a negative effect on the health of a cancer pa-

tient. If a user is provided with incorrect treatment in-

formation, the searcher may not seek known effective

treatments or may reject such beneﬁcial treatments in

favor of unhelpful treatments.

The prior study (Ghenai et al., 2020) was biased

towards highly educated young computer savvy part

of the population due to the recruiting process. How

would the effect of search results differ when deal-

ing with a different set of population with potentially

high risk of being inﬂuenced with the incorrect in-

formation such as older adults? In 2010, it was re-

ported that 45% of older adults over the age of 65 go

online to search for health-related information (Fox,

2010). While older adults often have lower health

literacy than younger adults (Czaja and Lee, 2012),

many of them are novices in Internet searching and

health-related websites (Becker, 2004). These factors

raise the concern of how incorrect online health infor-

mation effect older adults’ health-related decisions.

We advocate conducting a new study in order

to investigate how search results affect older adults

when determining the effectiveness of a medical treat-

ment. The central recommendation is to assist older

adults with challenges resulting from search engine

results by ﬁrst conducting a hands-on investigation in

order to properly ascertain the preferences and con-

cerns of this user base. We advocate combining sur-

veys, interviews in person, audio-recording, video-

recording and recording eye movements using an eye-

ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health

198

tracking devices. Only older adults capable to using a

computer to do online search (independently or with

help) will be able to participate. Each participant

also needs to conﬁrm that they are 65 years of age

or older.

With older adult users, any online questionnaires

will need to take into consideration vision function,

cognitive function and memory function; it would be

best as well to try an array of ages and to survey both

males and females. An important study (Seo et al.,

2020) has also highlighted that certain segments of

this older population are even more vulnerable to on-

line misinformation. This then suggests a compre-

hensive assembly of participants with different user

proﬁles.

We acknowledge that during the Covid pandemic,

it will now be more difﬁcult to gather together with

participants in person. But we would still like to learn

how older adults may be misled about this impor-

tant subtopic. We recommend trying to ﬁrst under-

stand which topics regarding health these users tend

to search for the most and what platforms they use the

most for health information gathering. It would also

be useful to determine, for Covid19, whether features

that social media plaforms have deployed in order to

mitigate this use (e.g. linking explicitly to trusted

sources) are helpful to older adults. We propose con-

ducting an online survey in order to get responses

from our users. We also feel it would be valuable to

conduct a longitudinal study where we conduct the

survey multiple times during a given time period, to

see if opinions change over time. We are also aware of

important research on how best to design surveys that

operate with older adult users and have some recom-

mendations following these results (displayed in ital-

ics), as well. We recommend that researchers need to

consider the technical dexterity of the users and that

responses rates to surveys may be increased if privacy

protection measures are heightened. A very interest-

ing study (Chin et al., 2018) focused speciﬁcally on

polling older adults with respect to healthcare infor-

mation. The authors conclude that at times new web-

sites arise which provide clearer advice to patients.

One useful strategy may be to display both old and

new websites when health information has been up-

dated, in order to emphasize the new contributions

for the older adult population.

3 THE POTENTIAL OF AI TRUST

MODELING

In order to examine in closer detail how algorithms for

judging trustworthiness of agents in multiagent sys-

tems may be of use in order to assist older adults on-

line with healthcare misinformation, we ﬁrst conduct

a deeper investigation of how this particular user base

differs from others, when considering trust in other

parties. One observation in the literature is that older

adults have smaller social circles, being less likely

to build new online contacts of their own initiative

(Steijn, 2014). This suggests that their conclusions

when presented with healthcare information online,

may have a tendency to agree with other users having

similar backgrounds. In this way, the effects of misin-

formation may acccumulate. Another distinguishing

viewpoint of the older adult community outlined in

uders and Brandtzæg, 2017) is that at times they

regard the Internet more as a functional tool than as

an avenue for social interaction. An outcome may be

less critical thinking of the content that is provided.

It is very important to treat each online user as an

individual and not to make generalizations based on

common habits that may be observed. Nonetheless,

when providing assistance online to someone iden-

tiﬁed as an older adult, some options may be pro-

vided which will facilitate beneﬁcial outcomes. Es-

pecially for the consideration of online healthcare

information, all users would beneﬁt from including

known experts in the medical ﬁeld in their social cir-

cle. Older adults may acquire more discrimination

of online content, if they were shown some statistics

of how many other users within their platform have

disagreed with the opinion that they may choose to

promote (e.g. retweet). Some of the online statis-

tics that are not obviously displayed can become more

prominent. In part, this suggestion aligns with earlier

recommendations of ours about fake reviews (Section

2.2), to enable older adults to be more aware of true

negatives rather than focusing on positives. In a sim-

ilar vein, the information in online social networks,

when directed to users identiﬁed as older adults, can

draw out more explicitly the origin of the advice that

is being provided (e.g. if a medical organization

known worldwide has initiated the information).

Two central approaches for reasoning about trust-

worthiness of online sources using multiagent trust

modeling have emerged recently. The ﬁrst outlines

how discussions which emerge following a post in

a social media environment such as Reddit can pro-

vide important clues as the reputation of the post

that launched the discussion (Parmentier and Cohen,

2019). The second sheds light on how clusters of

users with similar behaviours may receive person-

alized recommendations for whom to trust within a

review-based context, using Yelp as the exemplar

(Parmentier and Cohen, 2020). This is based on a

multi-faceted set of trust indicators such as how often

e-Health for Older Adults: Navigating Misinformation

199

a trustee’s ratings agreed with that of the trustor.

In order to move forward with algorithms such

as these, if assisting the user base of older adults in

particular, there are some key ideas which hold some

promise. The Reddit study explored the contributions

of speciﬁc features in the text such as negative lan-

guage or sentiment. But one interesting feature which

seemed to be particularly helpful in identifying ques-

tionable reputation was the difference in scores within

the ensuing discussion of a post. We have already

mentioned that older adults may need more assistance

with negativity when reacting online. As such, sce-

narios where older adults are involved in the discus-

sion may well be ones with signiﬁcant disagreement

amongst users. Further study of this particular indi-

cator of user reputation may be quite fruitful, in or-

der to provide assistance to older adults. The Yelp

study located clusters of users based on their pat-

terns of trusting others. But we may be trying to

assist a set of users already identiﬁed as all being

older adults. We may know the general features of

this set of users, such as ones we have drawn out in

this paper regarding attention on negative reactions.

This may then suggest new trust indicators to be used

within the multi-faceted trust model which may then

assist in grouping together older adult users within the

same clusters. In addition, what is known beforehand

about these users can be considered to be a prior on

the Bayesian reasoning employed within the proposed

multi-faceted trust modeling. Then the personalized

attention that is delivered can become appropriate ex-

plicitly for this user base.

4 LOOKING TO THE FUTURE

FOR OLDER ADULTS AND

ONLINE MISINFORMATION

In this paper, we have reported on some challenges

which arise when trying to assist older adult users

with online misinformation about healthcare. We

have made clear that different contexts are relevant

and important: social media, fake reviews and less

helpful search engine results. We have also explained

why older adult users may require special considera-

tion in each of these scenarios. And we have tried to

emphasize the additional concerns which arise when

the discussions centre around healthcare. For each

of the speciﬁc areas in which we would like to see

improved experiences for older adult users, we have

highlighted some speciﬁc recommendations: actions

to take, including the use of various automated, intel-

ligent algorithms and the importance of doing deeper

explorations of the actual needs and preferences of the

user base, online. Looking to the future, it would be

valuable to consider some more dramatic efforts for

more beneﬁcal outcomes for our older adult users, to-

wards their healthcare decisions. Several of these sug-

gestions emphasize improved interactions with this

user base.

A ﬁrst suggestion is to offer additional support

when the various technologies are being used. An

ambient help system which does not interrupt but al-

lows for additional guidance may be useful to install,

both with respect to search engine use and in navi-

gating posts in online social networks. The work of

(Matejka et al., 2011) serves as an inspiration for this

step forward. Some current efforts to engage older

adult users (Yu and Moffatt, 2019) have also shown

that the importance of being careful with these users

when it comes to layout preferences and privacy con-

siderations. In the case of healthcare, where messages

may seem mixed and difﬁcult to interpret, one cre-

ative suggestion to support older adults is to consider

integrating friendly voice assistants. We feel this so-

lution may be appealing, in much the same way that

robotic companions have been accepted (Bemelmans

et al., 2012). In general, we are suggesting that a

somewhat different emphasis on what to support and

consider for this user base may be required.

We also have speciﬁc plans for advancing from

solutions on detecting rumours about healthcare in

Twitter (Ghenai and Mejova, 2017). Taking Covid-19

as a case study (an issue of critical concern to older

adults), we can collect a set of tweets and then clas-

sify them into rumors or non-rumors. Later we can

scrutinize the source of the rumour tweets to better

understand the origins of this particular misinforma-

tion campaign. We can also collect Covid-19 arti-

cles from the RSS feeds of already labelled media

sources, as was suggested in a recent research chal-

lenge (ICWSM 2020 dataset challenge). After iden-

tifying sources which contain suspected misinforma-

tion, we can look further into whether certain domains

are more responsible for the spread. This then brings

us into the important concern of examining inﬂuence

spreaders within the networks (Starbird et al., 2018).

Since many studies to date have focused on the spread

of news misinformation, we would aim to study more

carefully whether the results differ when the focus is

on health misinformation.

With respect to Covid-19, it would also be instruc-

tive to learn whether opinions change over time, as

new sources of information are presented to users.

Towards this end, we would aim to have a longitu-

dinal study (same survey during different time peri-

ods) of older adult users. The speciﬁc exploration

ICT4AWE 2021 - 7th International Conference on Information and Communication Technologies for Ageing Well and e-Health

200

of Covid-19 is quite enlightening, as international

collaborations between fact checkers has been estab-

lished for this particular topic: 90 professionals from

39 countries have debunked 495 falsehoods in 15 lan-

guages. The #CoronaVirusFact/#DatosCoronaVirus

alliance has published six international reports in En-

glish and created a special search list on Twitter

(poy.nu/2019CoronaVirusFacts) to help citizens eas-

ily get the latest veriﬁed content online.

Moving from (Ghenai and Mejova, 2018) to con-

sider COVID-19 to detect misinformation, we raise

these points. First, Twitter is currently doing a

great effort to keep its content rumor-free regarding

COVID-19. As a result, when collecting data, we

might not ﬁnd a large amount of tweets containing

COVID-19 rumors due to stricter regulations regard-

ing the pandemic. Having fewer data might affect

the performance of the model. Second, (Ghenai and

Mejova, 2018) did not consider the geographic dis-

tribution of rumors in Zika or in the cancer study.

However, in COVID-19, we believe that we need to

consider the geographic location when building the

model. Many conspiracy theories and rumors might

be different in each location. The misinformation

tracking models in (Ghenai and Mejova, 2018) did not

look at temporal changes of rumors. Keeping track of

time when building the model will be important for

COVID-19, as regulations and rumors tend to change

rapidly.

5 FINAL REMARKS

This position paper has drawn attention to the fact

that the user base of older adults merits special con-

sideration, when developing technological solutions

to assist with misinformation about health that may

occur online. We make clear that different contexts

of use arise and need to be examined: social me-

dia, fake reviews and search engines. We also clar-

ify that for these online experiences, healthcare is a

prominent topic in focus for this base of users, who

may also be particularly vulnerable. Through some

speciﬁc recommendations about what to present and

what to highlight for these users and through some

original suggestions for moving forward with tech-

nologies that we have developed to date, with greater

focus on this user base, this position paper provides

some important messages and some concrete starting

points for progress. A predominant theme to this mes-

sage is enabling older adults who are online to be bet-

ter educated about potential misinformation. As such,

combining the steps forward outlined in this paper

with independent outreach to these individuals, mak-

ing them aware of valued sources and the importance

of careful scrutiny of online content, is also an essen-

tial part of the solution, in our view.

While what we discover now about older adults

may not be true tomorrow for the next generation

of these users, continuing to conduct dedicated user

studies will assist in understanding this demographic;

healthcare will always be an important issue for this

user base and helping to navigate misinformation

should therefore continue to be an ongoing concern.

REFERENCES

Bailey, P. E. and Leon, T. (2019). A systematic review and

meta-analysis of age-related differences in trust. Psy-

chology and aging, 34(5):674.

Becker, S. A. (2004). A study of web usability for older

adults seeking online health resources. ACM Trans-

actions on Computer-Human Interaction (TOCHI),

11(4):387–406.

Bemelmans, R., Gelderblom, G., Jonker, P., and de Witte,

L. D. (2012). Socially assistive robots in elderly

care: a systematic review into effects and effective-

ness. Journal of the American Medical Directors As-

sociation, 13 2:114–120.e1.

Chin, J., Moeller, D., Johnson, J., Duwe, E., Graumlich, J.,

Murray, M., and Morrow, D. (2018). A multi-faceted

approach to promote comprehension of online health

information among older adults. The Gerontologist,

58:686–695.

Czaja, S. J. and Lee, C. C. (2012). Older adults and in-

formation technology: opportunities and challenges.

The Human-Computer Interaction Handbook: Fun-

damentals, Evolving Technologies and Emerging Ap-

plications. Taylor & Francis, pages 825–40.

Fornaciari, T., Cagnina, L. C., Rosso, P., and Poesio, M.

(2020). Fake opinion detection: how similar are

crowdsourced datasets to real data? Language Re-

sources and Evaluation, pages 1–40.

Fox, S. (2010). Four in ten seniors go online.

Washington, DC: Pew Internet & American Life

Project. https://www.pewresearch.org/internet/2010/

01/13/four-in-ten-seniors-go-online/.

Ghenai, A. and Mejova, Y. (2017). Catching zika fever: Ap-

plication of crowdsourcing and machine learning for

tracking health misinformation on twitter. Proceed-

ings of ICHI, pages 518–518.

Ghenai, A. and Mejova, Y. (2018). Fake cures: User-centric

modeling of health misinformation in social media.

Proceedings of CSCW, 2:1–20.

Ghenai, A., Smucker, M. D., and Clarke, C. L. (2020). A

think-aloud study to understand factors affecting on-

line health search. In Proceedings of the 2020 Con-

ference on Human Information Interaction and Re-

trieval, pages 273–282.

Ghosh, D. and Iyengar, V. (2019). Older adults are espe-

cially prone to social media bubbles. Scientiﬁc Amer-

ican Blog Network.

e-Health for Older Adults: Navigating Misinformation

201

Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson,

B., and Lazer, D. (2019). Fake news on twitter

during the 2016 us presidential election. Science,

363(6425):374–378.

Guess, A., Nagler, J., and Tucker, J. (2019). Less than you

think: Prevalence and predictors of fake news dissem-

ination on facebook. Science Advances, 5.

Guidry, J. P., Jin, Y., Orr, C. A., Messner, M., and Meganck,

S. (2017). Ebola on instagram and twitter: How

health organizations address the health crisis in their

social media engagement. Public relations review,

43(3):477–486.

ajek, P., Barushka, A., and Munk, M. (2020). Fake con-

sumer review detection using deep neural networks in-

tegrating word embeddings and emotion mining. Neu-

ral Computing and Applications, pages 1–16.

Jia, S., Zhang, X., Wang, X., and Liu, Y. (2018). Fake

reviews detection based on lda. 2018 4th In-

ternational Conference on Information Management

(ICIM), pages 280–283.

Li, H., Fei, G., Wang, S., Liu, B., Shao, W., Mukherjee,

A., and Shao, J. (2017). Bimodal distribution and co-

bursting in review spam detection. Proceedings of the

26th International Conference on World Wide Web.

Liao, Q. V. and Fu, W.-T. (2014). Age differences in

credibility judgments of online health information.

ACM Transactions on Computer-Human Interaction

(TOCHI), 21(1):1–23.

Liu, W., He, J., Han, S., Cai, F., Yang, Z., and Zhu, N.

(2019). A method for the detection of fake reviews

based on temporal features of reviews and comments.

IEEE Engineering Management Review, 47:67–79.

uders, M. and Brandtzæg, P. (2017). ‘my children tell me

it’s so simple’: A mixed-methods approach to under-

stand older non-users’ perceptions of social network-

ing sites. New Media & Society, 19:181 – 198.

Matejka, J., Grossman, T., and Fitzmaurice, G. W. (2011).

Ambient help. In CHI.

Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M.,

Castellanos, M., and Ghosh, R. (2013a). Spotting

opinion spammers using behavioral footprints. In

KDD ’13.

Mukherjee, A., Venkataraman, V., Liu, B., and Glance, N.

(2013b). What yelp fake review ﬁlter might be doing?

In ICWSM.

Myhre, J. W., Mehl, M. R., and Glisky, E. L. (2016). Cogni-

tive Beneﬁts of Online Social Networking for Healthy

Older Adults. The Journals of Gerontology: Series B,

72(5):752–760.

Ott, M., Choi, Y., Cardie, C., and Hancock, J. T. (2011).

Finding deceptive opinion spam by any stretch of the

imagination. In Proceedings of the 49th Annual Meet-

ing of the Association for Computational Linguistics:

Human Language Technologies, pages 309–319.

Oyeyemi, S. O., Gabarron, E., and Wynn, R. (2014). Ebola,

twitter, and misinformation: a dangerous combina-

tion? Bmj, 349:g6178.

Parmentier, A. and Cohen, R. (2019). Learning user rep-

utation on reddit. In IEEE/WIC/ACM International

Conference on Web Intelligence, pages 242–247.

Parmentier, A. and Cohen, R. (2020). Personalized multi-

faceted trust modeling in social networks. In The

Canadian Conference on Artiﬁcial Intelligence.

Seo, H., Blomberg, M., Altschwager, D., and Vu, H. T.

(2020). Vulnerable populations and misinforma-

tion: A mixed-methods approach to underserved older

adults’ online information assessment:. New Media &

Society, page 146144482092504.

Shao, J., Du, W., Lin, T., Li, X., Li, J., and Lei, H. (2019).

Credulity rather than general trust may increase vul-

nerability to fraud in older adults: A moderated me-

diation model. Journal of elder abuse & neglect,

31(2):146–162.

Shuqin, Y. and Jing, F. (2019). Fake reviews detection based

on text feature and behavior feature. 2019 IEEE con-

ference on Data Science and Systems, pages 2007–

2012.

Starbird, K., Arif, A., Wilson, T., Van Koevering, K., Yeﬁ-

mova, K., and Scarnecchia, D. (2018). Ecosystem or

echo-system? exploring content sharing across alter-

native media domains. In Twelfth International AAAI

Conference on Web and Social Media.

Steijn, W. (2014). A developmental perspective regarding

the behaviour of adolescents, young adults, and adults

on social network sites. Journal of psychosocial re-

search, 8.

Stewart, R., Hotopf, M., Dewey, M., Ballard, C., Bisla, J.,

Calem, M., Fahmy, V., Hockley, J., Kinley, J., Pearce,

H., Saraf, A., and Begum, A. (2014). Current preva-

lence of dementia, depression and behavioural prob-

lems in the older adult care home sector: the south

east london care home survey. Age and ageing, 43

4:562–7.

Sturkenboom, M. C. (2015). The narcolepsy-pandemic in-

ﬂuenza story: can the truth ever be unraveled? Vac-

cine, 33:B6–B13.

Sun, H., Morales, A., and Yan, X. (2013). Synthetic review

spamming and defense. In KDD ’13.

von Helversen, B., Abramczuk, K., Kope

c, W., and Nielek,

R. (2018). Inﬂuence of consumer reviews on online

purchasing decisions in older and younger adults. De-

cision Support Systems, 113:1 – 10.

Wu, Y., Ngai, E., Wu, P., and Wu, C. (2020). Fake online

reviews: Literature review, synthesis, and directions

for future research. Decis. Support Syst., 132:113280.

Wylie, L. E., Patihis, L., and McCuller, L. L. (2014). Mis-

information effect in older versus younger adults: A

meta-analysis and review. In The elderly eyewitness

in court, pages 52–80. Psychology Press.

Yao, Y., Viswanath, B., Cryan, J., Zheng, H., and Zhao,

B. Y. (2017). Automated crowdturﬁng attacks and de-

fenses in online review systems. In Proceedings of

Conference on Computer and Communications Secu-

rity, pages 1143–1158.

Yu, J. and Moffatt, K. (2019). Improving accessibility of

social media for older adults. In Proceedings of CSCW

2019 Accessibility workshop.

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