Sleeping habits are one of the major issues in today’s healthcare. In this paper, we consider the problem of analyzing sleeping habits of people using social networking service (SNS) texts. As the first step toward predicting user’s sleeping time using SNS texts, we assume that the time span between the user’s last post in one day and the first post the next day can be used as a pseudo-indicator for the user’s sleeping time if the user posts the text sufficiently frequently. We call such tweet time spans “pseudo-sleeping time” if the first tweet of the next day include “Good morning” or similar words. We try to predict such pseudo-sleeping time using the text (tweet) of the preceding tweet (i.e., the last tweet of the day). Preliminary experiments show that the tweet text contains some useful information to predict the user’s pseudo-sleeping time

Kita, Kenji

Kojima, Takumi

Matsumoto, Kazuyuki

Yoshida, Minoru

English

Tokushima University Institutional Repository

International Journal of Advanced IntelligenceVolume 12, Number 1, pp.1-9, March, 2021.© AIA International Advanced Information InstituteToward Analyzing Relations between Sleeping Time and SocialNetworking Service Texts: Prediction of the Tweet Time SpanUsing the Last Tweet of the DayMinoru Yoshida, Takumi Kojima, Kazuyuki Matsumoto, and Kenji KitaTokushima University, Minamijosanjima-cho 2-1Tokushima, 770-8506, Japanmino@is.tokushima-u.ac.jpangel busters@icloud.commatumoto@is.tokushima-u.ac.jpkita@is.tokushima-u.ac.jpReceived (25 Oct. 2020)Revised (25 Dec. 2020)Sleeping habits are one of the major issues in today’s healthcare. In this paper, weconsider the problem of analyzing sleeping habits of people using social networking service(SNS) texts. As the first step toward predicting user’s sleeping time using SNS texts, weassume that the time span between the user’s last post in one day and the first post thenext day can be used as a pseudo-indicator for the user’s sleeping time if the user poststhe text sufficiently frequently. We call such tweet time spans “pseudo-sleeping time”if the first tweet of the next day include “Good morning” or similar words. We try topredict such pseudo-sleeping time using the text (tweet) of the preceding tweet (i.e., thelast tweet of the day). Preliminary experiments show that the tweet text contains someuseful information to predict the user’s pseudo-sleeping time.Keywords: sleeping time, SNS, text mining1. IntroductionIn this paper, we discuss the problem of predicting the sleeping time of a persongiven the social networking service (SNS) texts (e.g., tweets) of that person. Today,sleeping habits are one of the main issues in healthcare. It will contribute greatlyto our quality of life if we can predict or obtain some insights about our sleepinghabits from the SNS texts.However, it is costly to collect accurate sleeping time data, resulting in verysmall size of obtained data to be useful for meaningful analysis or applicable tomachine-learning algorithms.To solve these issues, we instead propose a concept of pseudo-sleeping time, whichcan be easily collected using time stamps of SNS posts. Here, pseudo-sleeping timeis defined as tweet time spans between the last tweet of one day and the first tweet ofthe next day. (See Figure 1.) Note that here “day” is not the span from 0:00 to 23:59hours, but the span of awake hours of a person. In other words, we regard the tweet12 M. Yoshida, T. Kojima, K. Matsumoto, K. KitaFig. 1. Definition of Pseudo Sleeping Timesaying “good morning” or similar things as the first tweet of a day (which we callgood-morning tweets) and the tweet before the first morning tweet as the last tweetof the previous day (which we call good-night tweets.) That is, the pseudo-sleepingtime is the time span between a good-night tweet and the next good-morning tweet.In this paper, we try to predict the pseudo-sleeping time using the contents ofthe good-night tweets using a standard support vector machine (SVM) classifier.We compare several settings using different datasets and different features to beused in the classifier.The remainder of the paper is organized as follows. In Section 2, we discussthe related work. Section 3 explains our data and Section 4 describes our methods.We show our experimental results using two types of data in Section 5. Section 6concludes the paper and lists the future work.2. Related WorkMany researches for using Twitter for disease surveillance have been done so far1.For example, visualizing influenza epidemic by extracting tweets related to influenzais a major research topic in this area6,7, while systems for mining more generalpublic-health-related issues were also available5. Surveying public health using Twit-ter is also a major research topic. Sadilek and Kautz3 proposed an automatic wayto infer the health of people using geo-tagged tweets. They used features such asthe word “sick” appearing in the tweets, the number of times the user visits gyms,etc.Analyzing people with sleep troubles using Twitter was proposed by some re-searchers. Jamison-Powell et al.2 used Twitter to find the people with their sleepdisorder by Twitter content analysis. (For example, tweets containing the word “in-somnia” strongly suggest that the user has sleep-related troubles.) McIver et al.4also proposed to use Twitter to investigate sleep issues by finding users who exhib-ited keywords like “can’t sleep” and analyze their tweets. These researches focusedRelations between Sleep Time and SNS Texts 3on people who have relatively serious troubles with sleep. In contrast, our researchfocuses on more general sleeping-time issues including simple insufficient sleepingtime, which is not a severe disease currently but may cause other related diseases.Our contribution especially is to propose a method for analyzing the tweets thatdo not contain sleep-related words explicitly (like “can’t sleep”) for sleeping timesurveillance.3. Data and Problem DefinitionWe collected tweets via Twitter REST API v1.1 and then searched for “good morn-ing” or other similar phrases. If the tweet m that contained such phrases (which wecall good-morning tweets) were found, we obtained the previous tweet n (which wecall good-night tweets), and calculated the time span t between m and n.In this paper, we define our prediction problem as the binary classification prob-lem. We give label l = +1 to the tweet n if t > 8 (i.e., pseudo-sleeping time is over 8hours) and label −1 otherwise. We call the tweets with label l = +1 positive tweetsand the tweets with label l = −1 negative tweets. Therefore, resulting (n, l) pairs(i.e., pairs of the good-morning tweet and time-classification label) are used as ourdataset and the task is to predict label l given n.4. MethodEach tweet is converted to the word list using the morphological analyzer, MeCaba.MeCab separates a sentence into a list of words, each of which is tagged with Part-Of-Speech (POS) labels such as nouns, verbs, and adjectives.We tested the following three types of POS filters:• nouns only,• verbs only, and• nouns and verbs only.After obtaining a list of words for each sentence, words other than the selectedPOS (e.g., adjectives or prepositions) are discarded. The resulting list is convertedto a Bag of Words (BoW) representation, which is a list of (word, frequency) pairs.The resulting BoW for each tweet is converted to tf-idf vectors by replacing afrequency value for each word with the corresponding tf-idf score. The tf-idf scoringfunction is defined as follows:tfidf(t, d) = tf(t, d) · idf(t, d)tf(t, d) and idf(t, d) are defined as follows:tf(t, d) =nt,d∑s∈d ns,dahttps://taku910.github.io/mecab/4 M. Yoshida, T. Kojima, K. Matsumoto, K. Kitawhere n(t, f) is the frequency of word t in document d.idf(f) = logNdf(t) + 1where N is the number of documents and df(t) is the number of documents in whicht appears.The obtained tf-idf vectors were divided into training and test data and usedfor classification. SVM8 was used as a standard classifier for binary classificationproblems. SVMs were trained on training data, and the accuracy of the trainedclassifier was evaluated on test data. We used the Classias9 implementation for theSVM classification.5. ExperimentsWe collected two types of data described as follows:Data-1: We collected good-morning tweets randomly using the Twitter search APIand obtained corresponding good-night tweets.Data-2: We collected good-morning tweets of selected users, and obtained corre-sponding good-night tweets.In the following subsections, we describe these datasets and reports the experi-mental results on each data set.5.1. Results on Data-1Data-1 is a collections of tweets randomly obtained by searching for good-morningtweets and corresponding good-night tweets. As a result, we obtained the followingnumber of tweets for training and test data.• Training data: 1,500 tweets• Test data: 100 tweetsWe applied SVM on this data. The results are shown in Table 1.Table 1. Results on Data-1nouns verbs nouns and verbs47% 83% 51%We observed that the setting using verb features showed extremely high accu-racy. To find the reasons, we checked what kinds of strings are found in positive(l = +1) or negative (l = −1) tweets.We found that the following phrases were frequently found in the negative tweets:Relations between Sleep Time and SNS Texts 5(1) “Fukugyou” (second job)(2) Japanese quotation marks(3) “Eigyou” ((shop is) open)(4) “Nyusu” (news)(5) “DM” (direct mail)These were typical strings found in bot accounts. For example, “Eigyou” (open)was found in the advertisement tweets such as “XX shop is open from 10:00 to22:00 !”. We also found that Japanese quotation marks were typically found in thebot accounts that quoted famous phrases or jokes. As a result, phrases that suggestthat the account was for the user’s lifelogs could be used as features to distinguishsuch lifelog tweets from the “noisy” bot tweets. Especially, verbs like “Neru” (sleep)strongly suggest that the accounts were for lifelogs, resulting in high accuracy forverb features.Considering these results, we concluded that the high accuracy obtained on thedata 1 was highly due to these “noises” that did not reflect the actual sleeping timeof the user.Therefore, we decided to construct a new data set (the data-2 mentioned above),which is “cleaner” in the sense that it:(1) Consisted of reliable users only.(2) Manually checked whether the good-night tweets are actually the last tweet ofthe previous day.To satisfy condition 1, we limited the collected accounts to be of the studentusers only because student users show relatively regular life patterns compared toadults who have much various statuses (e.g., office workers, housewives/husbands,self-employed workers, etc.), resulting in more meaningful analysis. We also focusedon the users with the sufficient numbers of tweets to be used for good-moriningand good-night tweets extraction. Condition 2 is to filter out the “noisy” tweetsthat is before the morining tweet but is not the good-night tweet (e.g., the pre-good-morining tweets that do not say “good-morining”, such as “I will sleep a littlelonger...”) Although currently we check the condition 2 manually, we think we canautomate the process using some patterns or classifiers to filter out such noisygood-night tweets.5.2. Results on Data-2In data-2, we obtained the user profile texts of the tweets, and retained the tweetsof the user whose profile contains the word “student.” It is for focusing on onlytweets by students for the purpose of regularizing the lifestyle habits of the users,because lifestyle habits are much different among people with various status/jobs,such as students and office workers.As a result, we obtained the following number of users, as shown in Table 2.6 M. Yoshida, T. Kojima, K. Matsumoto, K. KitaTable 2. The Number of Users and Tweetshhhhhhhhhhhhhhhh# of data# of data typestraining test# of users 50 10# of data per user 3,000 3,000# of all tweets 150,000 30,000We obtained good-morning and good-night tweets from these tweets, resultingin the following number of training and test data.Table 3. Description of Data-2training test1817 484We applied SVMs to the obtained data in the same way as data-1. Table 4 showsthe results.Table 4. Results on Data-2nouns verbs nouns and verbs62.3% 61.7% 63.3%We observed that we could predict the label l with over 60% accuracy. Thisresult suggests that the last tweet of a day contained some useful information forpredicting the user’s sleeping time.To analyze the results in further detail, we extracted phrases frequently foundin positive/negative tweets in the same way as data-1.“Baito” (part-time job) were very frequently found in positive tweets (19 timesin positive tweets while 0 times in negative ones). In contrast, various phrases werefound in negative tweets such as:(1) “Jikan” (time)(2) “Testo” (exam)(3) “2ji” (2 o’clock)(4) “tokei” (clock)(5) “neochi” (falling asleep)(6) “netai” (want to sleep)Phrase 1, 3, and 4 suggest that the user suddenly realized that it was late atRelations between Sleep Time and SNS Texts 7night. In contrast, phrases 2 and 6 suggest that they had work to do and thus couldnot go to bed. Phrase 5 suggests that the user fell asleep and would go to bed tosleep for the remaining time. In general, these situations tend to cause a reducedsleeping time.6. Conclusions and Future WorkIn this paper, we discussed the problem of predicting the sleeping time of personsusing Twitter texts posted by users. For collecting sufficient data at low costs, weproposed the concept of pseudo-sleeping time, which can be considered as a kind ofupper-bound of the true sleeping time and used as an approximation to the sleepingtime. Using this concept, we defined our task to classify the pseudo-sleeping timeusing the final tweet of the day.We applied SVM classifiers on different datasets using different features, andfound that we can predict the pseudo-sleeping time to some extent using appropri-ately collected datasets by avoiding “noises” (i.e., bot tweets) and constrained usertypes (i.e., students in our case).The future work includes analysis of the relation between the pseudo-sleepingtime and true sleeping time. We also plan to increase the data size by including moreusers. The use of classification methods other than SVMs will also be an importantfuture work.AcknowledgmentsThis work was supported by JSPS KAKENHI Grant Numbers JP18K11549,JP20K12027.References1. Lauren E Charles-Smith, Tera L Reynolds, Mark A Cameron, Mike Conway, Eric HY Lau,Jennifer M Olsen, Julie A Pavlin, Mika Shigematsu, Laura C Streichert, Katie J Suda, CourtneyD Corley, Using social media for actionable disease surveillance and outbreak management: asystematic literature review, PloS one, 10(10), 2015.2. Sue Jamison-Powell, Conor Linehan, Laura Daley, Andrew Garbett, Shaun W. Lawson: ”Ican’t get no sleep”: discussing #insomnia on twitter. CHI 2012: pp. 1501-1510, 2012.3. Adam Sadilek, Henry A. Kautz: Modeling the impact of lifestyle on health at scale. WSDM2013: pp. 637-646, 2013.4. David J. McIver, J. Hawkins, R. Chunara, Arnaub K Chatterjee, Aman Bhandari, TimothyP. Fitzgerald, S. Jain, J. Brownstein: Characterizing Sleep Issues Using Twitter, Journal ofMedical Internet Research, Vol 17, No 6, 2015.5. Michael J. Paul, Mark Dredze: You Are What You Tweet: Analyzing Twitter for Public Health.ICWSM 20116. Shin Kanouchi, Mamoru Komachi, Naoaki Oka-zaki, Eiji Aramaki, Hiroshi Ishikawa, Whocaught a cold ? - Identifying the subject of a symptom, Proceedings of ACL (1) 2015, pp.1660-1670, 2015.7. Hayate Iso, Shoko Wakamiya, Eiji Aramaki, Forecasting Word Model: Twitter-based InfluenzaSurveillance and Prediction, Proceedings of COLING 2016, pp. 76-86, 2016.8. Corinna Cortes, Vladimir Vapnik, Support-vector networks, Machine Learning vol. 20, pp.273?297. 1995.8 M. Yoshida, T. Kojima, K. Matsumoto, K. Kita9. Naoaki Okazaki, Classias: A collection of machine-learning algorithms for classification,http://www.chokkan.org/software/classias/Minoru YoshidaHe is a lecturer at the Department of Information Sci-ence and Intelligent Systems, University of Tokushima.After receiving his BSc, MSc, and PhD degrees from theUniversity of Tokyo in 1998, 2000, and 2003, respectively,he worked as an assistant professor at the InformationTechnology Center, University of Tokyo. His current re-search interests include web document analysis and textmining for the documents available on the WWW.Takumi KojimaHe received his bachelor’s and master’s degrees fromTokushima University in in 2018 and 2020, respectively.His research interests include natural language process-ing and social network analysis. He currently works atDOCOMO CS Shikoku Inc.Kazuyuki MatsumotoHe received his PhD degree in 2008 from TokushimaUniversity. He is currently an associate professor ofTokushima University. His research interests include af-fective computing, emotion recognition, artificial intelli-gence and natural language processing. He is a memberof IPSJ, ANLP, IEICE and IEEJ.Relations between Sleep Time and SNS Texts 9Kenji KitaHe received the B.S. degree in mathematics and PhDdegree in electrical engineering, both from Waseda Uni-versity, Tokyo, Japan, in 1981 and 1992, respectively.From 1983 to 1987, he worked for the Oki Electric In-dustry Co. Ltd., Tokyo, Japan. From 1987 to 1992, hewas a researcher at ATR Interpreting Telephony ResearchLaboratories, Kyoto, Japan. Since 1992, he has been withTokushima University, Tokushima, Japan, where he cur-rently is a Professor at Faculty of Engineering. His cur-rent research interests include multimedia information re-trieval, natural language processing, and speech recogni-tion.

Relations between Sleep Time and SNS Texts

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Relations between Sleep Time and SNS Texts

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