Integrated entitymetrics analysis for health information on bipolar disorder using social media data and scientific literature
| Date | 22 August 2022 |
| Pages | 535-560 |
| DOI | https://doi.org/10.1108/AJIM-02-2022-0090 |
| Published date | 22 August 2022 |
| Author | Tatsawan Timakum,Min Song,Giyeong Kim |
Integrated entitymetrics analysis
for health information on bipolar
disorder using social media data
and scientific literature
Tatsawan Timakum
Department of Information Science, Chiang Mai Rajabhat University,
Chiang Mai, Thailand, and
Min Song and Giyeong Kim
Department of Library and Information Science, Yonsei University,
Seoul, Republic of Korea
Abstract
Purpose –This study aimed to examine the mental health information entities and associations between the
biomedical, psychological and social domains of bipolar disorder (BD) by analyzing social media data and
scientific literature.
Design/methodology/approach –Reddit posts and full-text papers from PubMed Central (PMC) were
collected. The text analysis was usedto create a psychological dictionary. The text mining tools were applied to
extract BD entities and their relationships in the datasets using a dictionary- and rule-based approach. Lastly,
social network analysis and visualization were employed to view the associations.
Findings –Mental health information on the drug side effects entity was detected frequently in both datasets.
In the affective category, the most frequent entities were “depressed”and “severe”in the social media and PMC
data, respectively. The social and personal concerns entities that related to friends, family, self-attitude and
economy were found repeatedly in the Reddit data. The relationships between the biomedical and
psychological processes, “afraid”and “Lithium”and“schizophrenia”and “suicidal,”were identified often in the
social media and PMC data, respectively.
Originality/value –Mental health information has been increasingly sought-after, and BD is a mental illness
with complicated factors in the clinical picture. This paper has made an original contribution to comprehending
the biological, psychological and social factors of BD. Importantly, these results have highlighted the benefit of
mental health informatics that can be analyzed in the laboratory and social media domains.
Keywords Bipolar disorder, Mental health information, Information extraction, Social media mining, Health
informatics
Paper type Research paper
Introduction
In the age of the Internet, seeking and sharing health information is enabled through online
resources. People can search for advice concerning treatments and either connect with health
experts or people with similar experiences (Cartright et al., 2011). The online health content
enables people to understand and manage their health conditions. Not only those living with
sickness but also the health professional or researcher has been enabled to explore the useful
information and knowledge for diagnosis and treatment as well as for the development of
new research and practice. While the demand for health information has been growing
globally, health information and publications have been disseminated increasingly as well.
Entitymetrics
analysis for
health
information
535
Funding: This work was supported by the Ministry of Education of the Republic of Korea and the
National Research Foundation of Korea (NRF-2020S1A5B1104865).
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2050-3806.htm
Received 21 February 2022
Revised 19 May 2022
14 June 2022
Accepted 14 June 2022
Aslib Journal of Information
Management
Vol. 75 No. 3, 2023
pp. 535-560
© Emerald Publishing Limited
2050-3806
DOI 10.1108/AJIM-02-2022-0090
Presently, the sharply increasing number of social media users has led to people taking
advantage of social media for health information. Moreover, they use social media to share their
healthcare experiences and stories about their family struggles. Patients search for health
information via social media to learn about other peoples’experiences with their diseases
(Warden, 2017). Therefore, it has become a powerfultool that can promoteinformation-sharing
about healthcare, provide feedback from users and foster support systems (Moorhead et al.,
2013). The existence of social media communities also enables researchers to learn about the
health experiences of patients, understand their treatment preferences and potentially locate
new knowledge in the field of health science. On the other hand, in the scholarly community, the
development of science, laboratory and clinical (medical) data has been produced and
disseminated more progressively as well. Both clinical data and health social media data have
been of significant valuefor people; however, there is a gap in information between them. The
clinical descriptions combine the functional, morphologic and biomedical information, while the
health social media data contain the experiences and lifestyle information (Denecke, 2015).
Consequently, this gap needs to be solved between physicians and patients to be able to access
and understand the detailed knowledge of diseases and medication.
In the big data age, to examine large amounts of information for knowledge discovery, data
mining offers an opportunity for interested scientists to be involved in its sophisticated
technology. It can help to access and analyze the massive amounts of data that are needed to
understand, predict and intervene. In the field of health informatics, the advances of big data and
data mining have impacted the health information acquisition and processing of patient data and
laboratory research. Consequently, the health information professionals who have special
knowledge in health information resources have to comprehend the emerging sciences and
evolving roles to provide evidence-based information to support and serve researchers, healthcare
providers and the public with proper health information at a time when information is needed.
Several studies have shown that health information searching behaviors tend to be
focused on mental health topics. Giles and Newbold (2011,2013) found that Internet users
shared information on their mental health in online communities as well as searched for
advice on mental health conditions which can lead them to self-diagnose. Similarly, the
studies by Gowen (2013) and Lal et al. (2018) reported that young adults used the Internet to
access information and support their mental health, especially information related to
medications and diagnoses, and then used that information to help them with their care.Aref-
Adib et al. (2016) studied the online mental health information-seeking behavior of people
with psychosis and found that information about medication and its side effects was the most
common topic that they paid attention to. Moreover, the patients could improve their
knowledge and in-depth understanding of mental health problems from an online resource,
which could help them feel better and manage their experiences.
The burden of depression and other mental health conditions is increasing worldwide, and
more than 280 million people are affected by depression disorder and over 700,000 people die
due to suicide every year (WHO, 2021). It ascends as a result of various factors such as
genetics, life circumstances and other infections (Timakum et al., 2022). People affected with
mental health conditions can suffer greatly and function poorly in daily life. With this effect,
the demand for mental health information has been rising globally.
Bipolar disorder (BD) is a major mental illness and is difficult to accurately identify in clinical
practice due to complicated contributing factors in the clinical picture of BD, with sometimes
nonobvious symptoms occurring at the onset of disease, primarily during adolescence and early
adulthood.Thismeans that diagnosisis frequentlydelayedby many years (Leboyer and Kupfer,
2010). Half of allindividuals with BD attempt suicideat least once in their lifetime, withmany
succeeding in their attempts (Fagiolini et al., 2004). Treatment can be effective if the patient is
correctly diagnosed and it is initiated early. To date, laboratory research and scholarly literature
have been performed and published to assess BD outcomes and identify mental illnesses related
AJIM
75,3
536
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