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.Comment: 19 pages, 5 figures, 2 tables, accepted PLOS ON

Depression and Anxiety Detection from Blog Posts Data

Depressioon ja ärevus mõjutavad paljude inimeste elu ja kui diagnoos ei ole õigeaeg-selt määratud, võib see kaasa tuua märkimisväärseid terviseprobleeme ja isegi suitsiidi. Tänapäeval uurivad vaimse tervise spetsialistid ja andmeteadlased meetodeid, kuidas sotsiaalmeedia ja eriti avalikult kättesaadavate tekstisõnumite ja blogitekstide analüüsimise abil depressioonis inimesi tuvstada ja pakkuda neile vajalikku ravi ja toetust. Selles töös kogume eksperimentaalse andmestiku avalikult kättesaadavatest blogipostitustsest, mis koosneb nii kliinilisest kui ka kontrollgrupi postitustest. Kliiniline grupp koosneb autoritest, kes kannatavad depressiooni ja/või ärevuse all, kontrollgrupp koosneb tervetest isikutest, kes oma blogis kirjutavad depressiooni ja ärevuse teemadel. Töös leiame kogutud andmetes sisalduvad latentsed teemad ja analüüsime blogipostituste sisu vastavaltblogi autorite poolt kajastatud teemadele. Katsetame mitmete teksti kodeerimismeetoditega nagu sõnahulk (BOW), TFIDF ja teemamudelist tuletatud tunnused. Treenime tugivektormasinatel (SVM) ning konvolutsioonilistel närvivõrkudel (CNN) põhinevaid klassifikaatoreid kliinilisse ja kontrollgruppi kuuluvate autorite eristamiseks. Lisaks uurime, kuidas mõjutavad erineva pikkusega blogipostitused CNN’i klassifitseerimistäpsust. Parimad täpsuse ja saagise skoorid vastavalt 78% ja 0,72 saadi konvolutsioonilise närvivõrgu (CNN) klassifikaatoriga, mis oli initsialiseeritud eeltreenitud GloVe sõnavektoritega.Depression and anxiety affect the life of many individuals and if the diagnosis is notstated in time it could lead to considerable health decline and even suicide. Nowadays,mental health specialists, as well as data scientists, work towards analyzing socialmedia sources and, in particular, publicly available text messages and blogs to identifydepressed people and provide them with necessary treatment and support. In this work,we adopt an experimental data collection approach to gather a corpus of blog posts fromclinical and control subjects. Ill people are considered as clinical subjects while controlsubjects refer to healthy individuals. We inspect the latent topics found in collecteddata to analyze the blog’ content according to themes covered by blog authors. Weexperiment with various text encoding techniques such as Bag-of-Words (BOW), TermFrequency-Inverse Document Frequency (TFIDF) and topic model’s features. We applySupport Vector Machines (SVM) and Convolutional Neural Network (CNN) classifiersto discriminate between clinical and control subjects. Additionally, we explore theclassification performance of CNNs trained on blog post texts of different size. Thebest accuracy and recall scores of 78% and 0.72 respectively were obtained with aConvolutional Neural Network (CNN) classifier initialised with pretrained GloVe wordvector

Expanded Behavioral Model for Online Support Services

The rapid expansion of online technologies and health resources has created opportunities to develop broadly available interventions to address the needs of the modern patient. This study proposes a theoretical structure based on Andersen\u27s Individual Determinants of Health Service Utilization Model to describe who is using online support and how it is being used. Also unique to this analysis was the use of objective behavioral data to describe and predict website utilization, and linguistic analysis to evaluate the content of what is shared in online groups. Eighty-four men and women completed baseline evaluations and were randomized into either online support or a waiting-list control condition. The overall theoretical structure did not produce a significant model; however the individual variables education, past online experience, and time spent online were predictive of participation. The somewhat unexpected finding that those with no prior online group experience and those who were high school educated were more likely to participate is discussed. If replicated, these findings may lend support to the idea that online interventions could provide needed support to individuals who do not typically participate in face-to-face interventions, and that the barriers to online group participation are not the same as the barriers for face-to-face group participation

Controversy trend detection in social media

In this research, we focus on the early prediction of whether topics are likely to generate significant controversy (in the form of social media such as comments, blogs, etc.). Controversy trend detection is important to companies, governments, national security agencies, and marketing groups because it can be used to identify which issues the public is having problems with and develop strategies to remedy them. For example, companies can monitor their press release to find out how the public is reacting and to decide if any additional public relations action is required, social media moderators can moderate discussions if the discussions start becoming abusive and getting out of control, and governmental agencies can monitor their public policies and make adjustments to the policies to address any public concerns. An algorithm was developed to predict controversy trends by taking into account sentiment expressed in comments, burstiness of comments, and controversy score. To train and test the algorithm, an annotated corpus was developed consisting of 728 news articles and over 500,000 comments on these articles made by viewers from CNN.com. This study achieved an average F-score of 71.3% across all time spans in detection of controversial versus non-controversial topics. The results suggest that it is possible for early prediction of controversy trends leveraging social media

LIFE ON HOLD: The effect of recession and neoliberalism on millennials’ beliefs about education, economic participation, and adulthood

Americans born between 1982 and 1995 (the “millennial” generation) are coming of age and entering adulthood during a time of historically significant economic recession. This study uses qualitative data derived from online sources to explore the effect of this experience on the values, feelings, and beliefs of these young adults regarding economic participation, education, and adulthood. Results indicate that millennials feel isolated, ashamed, fearful, and angry about their circumstances. Some feel so hopeless that mental health problems result. The neoliberal ideology widely prevalent in American culture is identified as a strong contributing factor to this situation. Educators are encouraged to recognize this reality and address structural reasons for the situation, alleviating the self-blame and shame young people experience

Forms of World Literature and the Taipei Poetry Festival

In poetry anthologies and works of literary criticism, the authority to select which literature can become “world” literature often lies with a single editor or theorist. This essay contrasts those centralizations of authority with the more egalitarian structure of international poetry festivals. Using the 2016 Taipei Poetry Festival as an example, the essay reads the impact of the form of the festival on its audience’s experience of translation, the local in the transnational, and intercultural solidarity. The essay then argues that boredom is a formal flaw in contemporary festivals, and advocates that translations be performed in local vernaculars