Resources
- Scientific Reports, https://academic.oup.com/joc
- Journal of Communication, https://academic.oup.com/joc
- Information Systems Research, https://pubsonline.informs.org/journal/isre
- MIS Quarterly, https://misq.umn.edu/
- Social Networks, https://www.sciencedirect.com/journal/social-networks
- Computational Humanities Research, https://www.cambridge.org/core/journals/computational-humanities-research
- EPJ Data Science, https://epjdatascience.springeropen.com/
- International Conference on Computational Social Science (IC²S²), https://iscss.org/about/about-iscss/
- Computational Humanities Research, https://2024.computational-humanities-research.org/
- Social Science Data Lab at Berkely, https://dlab.berkeley.edu/
- Centre for Digital Humanities at Princeton, https://cdh.princeton.edu/
- Computational Social Science Lab at Penn, https://css.seas.upenn.edu/
- Centre for Computational Social Science (UBC), https://ccss.arts.ubc.ca/
- Swedish Excellent Centre for Computational Social Science, https://liu.se/en/research/swecss
- Cultural Analytics Lab (University of Amsterdam), https://canal-lab.nl/http://www.melvinwevers.nl/
- Distant Viewing Lab (University of Richmond), https://distantviewing.org/
- Cultural Data Analytics Lab (Tallinn University), https://cudan.tlu.ee/
- Copenhagen Centre for Social Data Science, https://sodas.ku.dk/
- Cambridge Digital Humanities, https://www.cdh.cam.ac.uk/
- Digital Humanities @ Oxford, https://digital.humanities.ox.ac.uk/home
- Computational Social Science Lab (University of Sydney), https://computational-social-science.sydney.edu.au/
- Summer Institutes in Computational Social Science, https://sicss.io/
- Berkely’s DLAB Computational Social Science Training Program, https://github.com/dlab-berkeley/Computational-Social-Science-Training-Program
- Jae Yeon Kim’s Computational Thinking for Social Scientists, https://github.com/jaeyk/comp_thinking_social_science
- Jorge Cimentada’s Machine Learning for Social Scientists, https://cimentadaj.github.io/ml_socsci/
- Chris Bail’s Computational Social Science (Duke University) https://cbail.github.io/comp_soc_grad/
- Data Science for Social Good (Carnegie Mellon University), https://github.com/dssg
- Singapore Social Science and Humanities Research Council, https://www.ssrc.edu.sg/
- National Research Foundation, https://www.nrf.gov.sg/
- National Heritage Board, https://www.nhb.gov.sg/what-we-do/our-work/community-engagement/grants
- Textual and Bibliographic Databases (PhilComp), https://www.philocomp.net/texts/databases.htm
- Resources for Computational Social Science (University of Chicago), https://guides.lib.uchicago.edu/compsocsci/datasets
- Humanities Datasets (Princeton), https://cdh.princeton.edu/programs/humanities-data-new/
- A tool for finding distinguishing terms in corpora and displaying them in an interactive HTML scatter plot, https://github.com/JasonKessler/scattertext
- Protest Activity Detection and Perceived Violence Estimation from Social Media Images, https://github.com/wondonghyeon/protest-detection-violence-estimation
- Word2Vec-bias-extraction, https://github.com/arsena-k/Word2Vec-bias-extraction
- SAGE Tool list for Social Science, https://github.com/sagepublishing/sage_tools_social_science/blob/master/data/master_tools_current.csv
- A curated lists of resources across various machine learning and deep learning topics, https://github.com/ZhiningLiu1998/awesome-awesome-machine-learning#table-of-contents
- Understanding Neural Networks, https://playground.tensorflow.org
- Demo to play with and understand neural networks, https://poloclub.github.io/cnn-explainer/