Keynote Speaker II

Abstract Biomedical engineering research trend can be healthcare models with unobtrusive smart systems for monitoring vital signs and physical activity. Detecting infant facial cry because of inability to communicate pain, recognizing facial emotion to understand dysfunction mechanisms through micro expression or transform captured human expression with motion device into three-dimensional objects are some of the applied systems. Nowadays, collaborated with biomedical research, mining and analyzing social network can improve public and private health care sectors as well such as research health news shared on social media about pharmaceutical drugs, pandemics, or viral outbreaks. Due to the vast amount of shared news, there is an urgency to select and filter information to prevent the spread of hoax or fake news. We explored in depth some steps to classify hoaxes written as news articles. This discussion also encourages on how technologies of social network analysis could be used to make new kinds improvement in health care sectors. Then close with a description of limitless future possibilities of biomedical engineering research in social media.

[1]  Mauridhi Hery Purnomo,et al.  HIDDEN MARKOV MODELS BASED INDONESIAN VISEME MODEL FOR NATURAL SPEECH WITH AFFECTION , 2016 .

[2]  Mauridhi Hery Purnomo,et al.  Limitless possibilities of pervasive biomedical engineering: Directing the implementation of affective computing on automatic health monitoring system , 2016, 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE).

[3]  Eugenio Tacchini,et al.  Some Like it Hoax: Automated Fake News Detection in Social Networks , 2017, ArXiv.

[4]  S L Jones Is it true? , 1994, Archives of psychiatric nursing.

[5]  Isabelle Augenstein,et al.  A simple but tough-to-beat baseline for the Fake News Challenge stance detection task , 2017, ArXiv.

[6]  Hal Berghel,et al.  Alt-News and Post-Truths in the "Fake News" Era , 2017, Computer.

[7]  Kalina Bontcheva,et al.  Stance Detection with Bidirectional Conditional Encoding , 2016, EMNLP.

[8]  Victoria L. Rubin Deception Detection and Rumor Debunking for Social Media , 2017 .

[9]  Errissya Rasywir,et al.  Eksperimen pada Sistem Klasifikasi Berita Hoax Berbahasa Indonesia Berbasis Pembelajaran Mesin , 2016 .

[10]  Sang Hoon Lee,et al.  Biomedical Engineering: Frontier Research and Converging Technologies , 2016 .

[11]  Fabricio F Costa,et al.  Social networks, web-based tools and diseases: implications for biomedical research. , 2013, Drug discovery today.

[12]  Andreas Vlachos,et al.  Emergent: a novel data-set for stance classification , 2016, NAACL.

[13]  Yimin Chen,et al.  Automatic deception detection: Methods for finding fake news , 2015, ASIST.

[14]  Jure Leskovec,et al.  Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes , 2016, WWW.

[15]  Neel Rakholia “ Is it true ? ” – Deep Learning for Stance Detection in News , 2017 .

[16]  MAURIDHI HERY PURNOMO,et al.  AUTOMATIC LIP READING FOR DAILY INDONESIAN WORDS BASED ON FRAME DIFFERENCE AND HORIZONTAL-VERTICAL IMAGE PROJECTION , 2017 .

[17]  Esther Irawati Setiawan,et al.  A Novel Approach on Infant Facial Pain Classification using Multi Stage Classifier and Geometrical-Textural Features Combination , 2017 .