プロフィール詳細

プロジェクトを作成

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Dr. Tomoko O.に依頼

Germany

Data Scientist & Policy Analyst | Climate · AI · Science & Technology | Large-Scale Text Analysis · Survey & Experiment

プロフィール概要

I am a computational social scientist and data scientist specializing in large-scale NLP-based analysis of public opinion on climate change, AI governance, and science & technology policy. My work combines transformer-based text analysis (RoBERTa, BERTweet, STM), multilevel statistical modeling (HLM, SEM), and survey and experimental methods — applied to datasets exceeding one million records from social media platforms. I translate complex quantitative findings into policy-relevant insights for government stakeholders and international audiences, and am now seeking applied data science and policy analytics roles in international organizations, think tanks, and consulting.

専門分野

• ☆ Data Analysis
• ☆ Social Network Analysis
• ☆ Data Processing
• ☆ Data Exploration
• ☆ Cluster Analysis
• ☆ Data Visualization
• ☆ Big Data Analytics
• ☆ Data Collection
• ☆ Data Management
• ☆ Data Manipulation
• ☆ Machine Learning
• ☆ Text Mining & Analytics
• ☆ Data Mining
• ☆ Data Cleaning
• ☆ Natural Language Processing
• ☆ Neural Networks
• ☆ Deep Learning
• ☆ Statistics
• ☆ Applied Statistics
• ☆ Social Statistics
• ☆ Regression Analysis
• ☆ Multivariate Analysis
• ☆ Nonlinear Regression
• ☆ Bayesian Statistics
• ☆ Correlation Analysis
• ☆ Survey Design
• ☆ Study Design
• ☆ Statistical Review
• ☆ Spatial Statistics
• ☆ Health Data Analysis
• ☆ Human Behavior & Psychology
• ☆ Health Psychology
• ☆ Political Psychology
• ☆ Applied Psychology
• ☆ Educational Psychology
• ☆ Social Psychology
• ☆ Social Network Theory & Analysis
• ☆ Media Studies
• ☆ Media Literacy
• ☆ Political Campaigns
• ☆ Identity
• ☆ Political Sociology
• ☆ Communication Theory
• ☆ Asian Languages
• ☆ Asian Studies
• ☆ Social Commentary & Opinion
• ☆ Python
• ☆ SPSS
• ☆ SQL
• ☆ Excel
• ☆ SAS
• ☆ Tensorflow
• ☆ STATA
• ☆ LaTex
• ☆ R
• ☆ Tableau
• ☆ Scikit-learn
• ☆ Markup Languages
• ☆ Hypothesis
• ☆ Peer Review
• ☆ Research Methodology
• ☆ Survey Design & Methodology
• ☆ Meta-Research
• ☆ Fact Checking
• ☆ Literature Search
• ☆ Gap Analysis
• ☆ Instructional Webinars
• ☆ Technical & Scientific Writing
• ☆ Journal Selection
• ☆ Manuscript & Journal Article Writing
• ☆ Instructional Materials
• ☆ Release Notes
• ☆ Experimental Protocols
• ☆ White Papers
• ☆ Case Studies
• ☆ Textbook Content Writing
• ☆ Science Communication
• ☆ Literature Review
• ☆ Traditional/Narrative Literature Review
• ☆ Systematic Literature Review
• ☆ Scientific Proofreading & Editing
• ☆ Proofreading & Editing
• ☆ Language Translation
• ☆ Market Research
• ☆ Qualitative Market Research
• ☆ Quantitative Market Research
• ☆ Project Management

サービス

• Data & AI

Data & AI Statistical Analysis, Data Visualization, Big Data Analytics, Text Mining & Analytics, Data Mining, Data Cleaning, Data Processing, Data Insights

職務経験

Post-Doctoral Researcher

Wissenschaftszentrum Berlin für Sozialforschung (WZB), Social Science Center, Berlin

7月 2022 - 現在

Doctoral Scholar

University of Wisconsin Madison

9月 2014 - 1月 2022

学歴

Ph.D. (School of Journalism and Mass Communication)

University of Wisconsin-Madison

- 現在

Ph.D, Mass Communications, (minor in Quantitative Methods in Social Sciences)

University of Wisconsin-Madison

1月 2026 - 1月 2026

認定資格

• 認定資格の詳細は未入力です。

出版物

JOURNAL ARTICLE

Tomoko Okada, Liwei Shen, Porismita Borah, Sijia Yang (2025). Populism, Community-Based Inequalities, and Public Health Policies: How Did Populist Attitudes Determine the COVID-19 Preventive Policy Support in Different Local Socioeconomic and Health Contexts? . International Journal Of Public Opinion Research.

Tomoko Okada, Jianing Li, Porismita Borah, Jiwon Kang, Jisoo Kim, Liwei Shen, Ran Tao, Sijia Yang (2025). Does news literacy help combat misinformation? The interplay of news literacy, political ideology, and ideological media use on COVID-19 misperceptions . Information, Communication & Society.

Okada, T., & Shen, L. (2023). Politicized science and rural–urban divides: Exploring how rurality in the place of residence and red and blue media use affected attitudes toward scientists before and during the COVID-19 pandemic. Analyses of Social Issues and Public Policy, 1–16. https://doi.org/10.1111/asap.12364 .