Information ScienceIMPACT
Social Computing
Prof. Eiji Aramaki

Automatically readinginternet information
to track viral infection trends

12Analyzing language data

Twitter is a powerful source of information

Conversations on social media on the Web, such as Twitter,
represent trends of phenomena influencing social life, e.g., influenza epidemics, at a certain point in time. The Social Computing
Laboratory, led by Professor Eiji Aramaki, is conducting research todevelop technologies for investigating various social phenomena
based on language data that manifest according to communication
patterns on social media. Aramaki’s group is also conducting
research that can be utilized in less-explored areas of information
science, e.g., the applications of natural language processing inmedical care.

Aramaki is supporting efforts to understand the infection status of
the novel coronavirus that has broken out worldwide. “Regarding
the infection-related information posted on the internet throughTwitter, we are investigating whether locally concentrated cases are
clusters (small outbreaks) and analyzing information that could beviewed as wild rumors and fake news,” Aramaki explains. Aramaki’s group provides their research data to national research
institutes for reference. The amount of retrieved information in
English, Chinese, and Japanese, with the words “novel coronavirus”
and related terms as a clue, reaches about 10,000 per day.

NAIST Research Highlights

Improving accuracy byremoving noise
Furthermore, Associate Professor
Shoko Wakamiya has proposed a
calculation model that considers
the gap between the estimatednumber of patients with tweets andthe real number of patients toimprove the accuracy of thedeveloped method. The research
paper describing this model wasawarded best paper by the International Medical Informatics Association (IMIA) in 2018. “Whenexamining a particular area, thenumber of tweets decreases once
the information is posted because
the people’s interest fades, whereas a temporary surge of tweets isobserved after a news report. Therefore, we compensated for themby additionally considering information about the infection posted
from other areas and also considering components that become
noise in the count,” Wakamiya explains. The subjects of the surveys
conducted so far are wide-ranging, including infectious foodpoisoning due to norovirus and hay fever. Recently, studies are also
conducted based on symptoms such as fever, chills, diarrhea, andrunny nose, attempting to narrow down the characteristics of evenunknown viruses based on symptoms and aiming to investigate theconditions of infection at an early stage.
“We are exploring the possibility of acquiring effective data by
expanding our methods, such as selecting frequently searchedwords on internet search engines, detecting the location history ofpeople who have installed specific applications on their smartphones and examining applications that allow the patient to report
actively,” Aramaki says.

characteristics when an individual speaks freely. Based on examination data collected at hospitals, certain speech characteristics signify
dementia, such as “a reduced amount of vocabulary (the totalnumber of words) and nouns” and “delayed utterance.” Based onthese data, a dementia-screening system called KOTOBAKARI has
been developed. The simplified version of this examination method
automatically calculates the number of words in a response to aquestion; e.g., “Have you had any fun lately?” If the calculated value
is significantly lower than the reference, it implies a decline incognitive function.
“A decline in cognitive function is thought to be related not only
to dementia but also to depression in elderly people and to the side
effects of anticancer drug treatment.
Thus, through the proposed system, weare additionally examining therelationship between diseases such as
cancer and decline in cognitivefunction,” Aramaki explains.

Information Science

Aramaki has proven that social media can become an influential
source of information for creating countermeasures for andpreventing infectious diseases, including influenza. First, theproposed method automatically infers the meaning of a sentence
using a natural language processing technique that converts vague
expressions from everyday conversation into language that can beunderstood by computers. Then, keywords such as “influenza” andsecondary terms such as “feverish” and “coughing” are searched,and extracted data are classified using artificial intelligence (AI).
Based on location information obtained from Twitter, the areas
showing more tweets are identified, enabling the prediction ofincreased infections. When the results based on the tweet data werecompared with data on thenumbers of patients released by theNational Institute of Infectious
Diseases, the correlation was sohigh as to almost match perfectly,
thereby receiving global attention.

Studying ethical issues
Research is also underway for cases
requiring long-term treatment, such asbreast cancer. A website called
EPISODE BANK has been established,
wherein patients are encouraged topost their experiences of fighting
illnesses to share and ease theirconcerns. Furthermore, the nation’s
first large-scale analysis technology is
being developed that analyzes large
quantities of clinical data, including
specialized medical terms, through anatural language processing technique,extracts accurate information andturns them into text using AI.
As research on medical treatment such as the handling ofmedical examinations and clinical data is being undertaken with
doctors as well as other medical personnel, it is expected that thefield of information science will expand into the medical field in thefuture. Therefore, collaborative research is also being conductedwith biotechnology research institutes regarding the ethical issues
related to handling personal medical information.
“Going forward, ethical issues pertaining to handling a varietyof information will become important. Furthermore, I want tocontinue to explore undeveloped fields, such as the connection
between information technology and creative concepts like art,
which AI cannot create,” Aramaki says.
Interviewed by Yoshinori Sakaguchi

Determining decline in cognitive function
Using techniques of speech recognition and natural language
processing, a study is currently underway on using medical care to
predict the difficult-to-detect signs of dementia by analyzing speechMore information about the group’s research can be found at https://isw3.naist.jp/Contents/Research/mi-08-en.html

NAIST Research Highlights

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