Japanese Blog Research Articles

Minor extensions of the logistic equation for growth curves of word counts on online media: parametric observation of diversity of growth in society

To understand the growth phenomena in collective human systems, we analyzed monthly word count time series of new vocabularies extracted from approximately 1 billion Japanese blog articles from 2007 to 2019. In particular, we first introduced the extended logistic equation by adding one parameter to the original equation and showed that the model can consistently reproduce various patterns of actual growth curves, such as the logistic function, linear growth, and finite-time divergence. Second, by analyzing the model parameters, we found that the typical growth pattern is not only a logistic function, which often appears in various complex systems, but also a non-trivial growth curve that starts with an exponential function and asymptotically approaches a power function without a steady state. We also observed a connection between the functional form of growth and the peak-out behavior. Finally, we showed that the proposed model and statistical properties are also valid for Google Trends data (English, French, Spanish, and Japanese), which is a time series of the nationwide popularity of search queries.

Relations between anomalous diffusion and fluctuation scaling: the case of ultraslow diffusion and time-scale-independent fluctuation scaling in language

Fluctuation scaling (FS) and anomalous diffusion have been discussed in different contexts, even though both are often observed in complex systems. To clarify the relationship between these concepts, we investigated approximately three billion Japanese blog articles over a period of six years and analyzed the corresponding Poisson process driven by a random walk model with power-law forgetting, which reproduces both the anomalous diffusion and the FS. From the analysis of the model, we have identified the relationship between the time-scale dependence of FS and characteristics of anomalous diffusion and showed that the time-scale-independent FS corresponds to essentially a logarithmic diffusion (i.e., a kind of ultraslow diffusion). In addition, we confirmed that this relationship is also valid for the actual data. This finding may contribute to the discovery of actual examples of ultraslow diffusion, which have been nearly unobserved in spite of many mathematical theories, because we can detect the time-scale-independent FS more easily and more distinctly than through direct detection of the logarithmic diffusion based on the mean squared displacement.

How religion and morality correlate in age of society 5.0: Statistical analysis of emotional and moral associations with Buddhist religious terms appearing on Japanese blogs

In this paper we analyzed how much religious vocabulary, in particular Buddhist vocabulary taken from the largest online dictionary of Buddhist terms, is present in everyday social space of Japanese people, particularly, in Japanese blog entries appearing on a popular blog service (Ameba blogs). We interpreted the level of everyday usage of Buddhist terms as appearance of such terms in the consciousness of people. We further analyzed what emotional and moral associations such contents generate. In particular, we analyzed whether expressions containing Buddhist vocabulary are considered appropriate or not from a moral point of view, as well as the emotional response of Internet users to Buddhist terminology. As a result of analyzing the data, we found out that Buddhist terms were in fact not absent as a theme from Japanese blogs and generated a strong emotional response. However, while the general reaction to several expressions using Buddhist terms was as expected, there were sometimes surprising twists in terms of social consequences, major discrepancies between what is perceived as ethically correct behavior between the Buddhist doctrine and the reasoning of the general population, as well as a considerate number of terms which have lost their original meaning and instead became slang expressions.

Identifying long-term periodic cycles and memories of collective emotion in online social media.

Collective emotion has been traditionally evaluated by questionnaire survey on a limited number of people. Recently, big data of written texts on the Internet has been available for analyzing collective emotion for very large scales. Although short-term reflection between collective emotion and real social phenomena has been widely studied, long-term dynamics of collective emotion has not been studied so far due to the lack of long persistent data sets. In this study, we extracted collective emotion over a 10-year period from 3.6 billion Japanese blog articles. Firstly, we find that collective emotion shows clear periodic cycles, i.e., weekly and seasonal behaviors, accompanied with pulses caused by natural disasters. For example, April is represented by high Tension, probably due to starting school in Japan. We also identified long-term memory in the collective emotion that is characterized by the power-law decay of the autocorrelation function over several months.

Estimation of Economic Indicator Announced by Government From Social Big Data.

We introduce a systematic method to estimate an economic indicator from the Japanese government by analyzing big Japanese blog data. Explanatory variables are monthly word frequencies. We adopt 1352 words in the section of economics and industry of the Nikkei thesaurus for each candidate word to illustrate the economic index. From this large volume of words, our method automatically selects the words which have strong correlation with the economic indicator and resolves some difficulties in statistics such as the spurious correlation and overfitting. As a result, our model reasonably illustrates the real economy index. The announcement of an economic index from government usually has a time lag, while our proposed method can be real time.

Statistical properties of fluctuations of time series representing appearances of words in nationwide blog data and their applications: An example of modeling fluctuation scalings of nonstationary time series.

To elucidate the nontrivial empirical statistical properties of fluctuations of a typical nonsteady time series representing the appearance of words in blogs, we investigated approximately 3×10^{9} Japanese blog articles over a period of six years and analyze some corresponding mathematical models. First, we introduce a solvable nonsteady extension of the random diffusion model, which can be deduced by modeling the behavior of heterogeneous random bloggers. Next, we deduce theoretical expressions for both the temporal and ensemble fluctuation scalings of this model, and demonstrate that these expressions can reproduce all empirical scalings over eight orders of magnitude. Furthermore, we show that the model can reproduce other statistical properties of time series representing the appearance of words in blogs, such as functional forms of the probability density and correlations in the total number of blogs. As an application, we quantify the abnormality of special nationwide events by measuring the fluctuation scalings of 1771 basic adjectives.

Expansion of medical vocabularies using distributional semantics on Japanese patient blogs.

BackgroundResearch on medical vocabulary expansion from large corpora has primarily been conducted using text written in English or similar languages, due to a limited availability of large biomedical corpora in most languages. Medical vocabularies are, however, essential also for text mining from corpora written in other languages than English and belonging to a variety of medical genres. The aim of this study was therefore to evaluate medical vocabulary expansion using a corpus very different from those previously used, in terms of grammar and orthographics, as well as in terms of text genre. This was carried out by applying a method based on distributional semantics to the task of extracting medical vocabulary terms from a large corpus of Japanese patient blogs.MethodsDistributional properties of terms were modelled with random indexing, followed by agglomerative hierarchical clustering of 3 ×100 seed terms from existing vocabularies, belonging to three semantic categories: Medical Finding, Pharmaceutical Drug and Body Part. By automatically extracting unknown terms close to the centroids of the created clusters, candidates for new terms to include in the vocabulary were suggested. The method was evaluated for its ability to retrieve the remaining n terms in existing medical vocabularies.ResultsRemoving case particles and using a context window size of 1+1 was a successful strategy for Medical Finding and Pharmaceutical Drug, while retaining case particles and using a window size of 8+8 was better for Body Part. For a 10n long candidate list, the use of different cluster sizes affected the result for Pharmaceutical Drug, while the effect was only marginal for the other two categories. For a list of top n candidates for Body Part, however, clusters with a size of up to two terms were slightly more useful than larger clusters. For Pharmaceutical Drug, the best settings resulted in a recall of 25 % for a candidate list of top n terms and a recall of 68 % for top 10n. For a candidate list of top 10n candidates, the second best results were obtained for Medical Finding: a recall of 58 %, compared to 46 % for Body Part. Only taking the top n candidates into account, however, resulted in a recall of 23 % for Body Part, compared to 16 % for Medical Finding.ConclusionsDifferent settings for corpus pre-processing, window sizes and cluster sizes were suitable for different semantic categories and for different lengths of candidate lists, showing the need to adapt parameters, not only to the language and text genre used, but also to the semantic category for which the vocabulary is to be expanded. The results show, however, that the investigated choices for pre-processing and parameter settings were successful, and that a Japanese blog corpus, which in many ways differs from those used in previous studies, can be a useful resource for medical vocabulary expansion.

Emoticons as a medium for channeling politeness within American and Japanese online blogging communities

This paper aims to examine how Brown and Levinson's (1987) politeness strategies are channeled through the medium of emoticons in American and Japanese online personal weblog comments. Results showed that although American and Japanese blog comment writers both utilized emoticons that helped to convey positive politeness strategies Japanese used them significantly more than Americans. It is argued that this challenges the notion of Japanese as being a negative orientated culture within this online blog community.

Beauty Between Empires: Global Feminism, Plastic Surgery, and the Trouble with Self-Esteem

Beauty Between EmpiresGlobal Feminism, Plastic Surgery, and the Trouble with Self-Esteem Sharon Heijin Lee (bio) Since 2012, when Korean rapper PSY’s “Gangnam Style” dominated US airwaves, television, and computer screens, the popularity of K-pop has created renewed interest among American media outlets and netizens in the topic of South Korean (hereafter Korea or Korean) plastic surgery consumption. The Atlantic featured a story on “The K-Pop Plastic Surgery Connection,” while Bloomberg News published on medical tourism in Korea: “Gangnam Style Nip and Tuck Draws Tourists to the Beauty Belt.”1 Buzzfeed’s story was more provocative, if strangely Eurocentric, asking “When Does Plastic Surgery Become Racial Transformation?”2 And most recently the New Yorker’s piece asks, “Why Is Seoul the World’s Plastic-Surgery Capital?”3 Other much more sensationalized reporting has produced images such as the “Miss Korea gif,” which went viral in less than forty-eight hours, appearing first on a Japanese blog, then Reddit, and then in national and international newspapers in April 2013. The gif, which compresses several still jpegs into moving images such that the beauty contestants’ faces morph one into the next at rapid speed, was meant to illustrate visually what Jezebel’s headline summed up as “Plastic Surgery Means Many Beauty Queens but Only One Kind of Face.”4 The gif—posted and reposted on social media sites such as Facebook, Twitter, and Reddit and reported upon by countless outlets—signified “Korea’s plastic surgery mayhem,”5 as one Reddit user described it. Such characterizations pathologize Korean cosmetic surgery consumption as a push toward racialized uniformity defined by a singular national beauty aesthetic across diverse Korean women’s faces.6 For US feminists, however, the gif took on particular salience. Mainstream feminist sites such as Jezebel offered the gif as evidence of a bizarre form of racialized patriarchal oppression happening abroad, making it distinct from, and exotic in relation to, forms of heteropatriarchal violence in the United States. As such, Jezebel reported on the Miss Korea gif, and Korean plastic surgery consumption more generally, at least four times in the spring [End Page 1] and summer months of 2013, in articles such as “I Can’t Stop Looking at These Korean Women Who’ve Had Plastic Surgery.” As it turned out, however, the individual images making up the viral gif were photoshopped.7 What was issued as objective visual evidence of Korean women’s fanatic obsession with plastic surgery instead reveals a fanatic obsession on the part of Americans for producing and consuming Korean women as such. Just a couple of months after the Miss Korea gif went viral in the United States, a Korean feminist nonprofit organization, Yŏsŏng Minuhoe (한국여성 민우회), known in English as Korean Womenlink (hereafter Womenlink), widely publicized a forum called “Apkujeong Station Exit #4: Let’s Talk about It.” A follow-up to their 2003 nationwide “Love Your Body” campaign, which sought to curb dieting and plastic surgery consumption among women, the forum’s title references the fact that nearly half of Seoul’s plastic surgery clinics are located in the Gangnam district (made world famous in PSY’s viral hit song), many of which can be accessed through the Apkujeong subway station and, more specifically, via the #4 exit from that stop.8 The forum was held in Seoul’s congressional building and sought public policy alternatives to curb a problem that the group asserts “has only gotten worse” since 2003.9 Besides Womenlink activists, the event featured a panel including a doctor, professor, television director, and lawyer, with a congressional representative giving closing remarks.10 The specialists provided insights into what they saw as the major factors fueling the cosmetic surgery industry as well as possible solutions to the problem. For Womenlink activists, it was a time to reflect on the ten years since their “Love Your Body” campaign. That Korean plastic surgery consumption would occupy the minds of Jezebel writers, editors, and millions of readers as well as Womenlink’s members, panelists, and forum attendees at roughly the same time—feminists from opposite ends of the world, so to speak—illuminates several key issues. First, such interest attests to the...

Mining personal experiences and opinions from Web documents

This paper proposes a new UGC-oriented language technology application, which we call experience mining. Experience mining aims at automatically collecting instances of personal experiences as well as opinions from vast amounts of user generated content (UGC) such as weblog and forum posts and storing them in an experience database with semantically rich indices. After discussing the technical issues relating to this new task, we focus on the central problem of factuality analysis, formulate a task definition, and propose a machine learning-based solution. Our empirical evaluation indicates that our factuality analysis defintion is sufficiently well-defined to achieve a high inter-annotator agreement and our Factorial CRF-based model considerably outperforms the baseline. We also present an application system, which currently stores over 50M experience instances extracted from 150M Japanese blog posts with semantic indices and serves an experience search engine for unrestricted users and report on our empirical evaluation of the system's accuracy.