Popularity Prediction for Social Media over Arbitrary Time Horizons
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
ACM CHI Conference on Human Factors in Computing Systems
People with dyslexia face challenges expressing themselves in writing on social networking sites (SNSs). Such challenges come from not only the technicality of writing, but also the self-representation aspect of sharing and communicating publicly on social networking sites such as Facebook. To empower people with dyslexia-style writing to express themselves more confidently on SNSs, we designed and implemented Additional Writing Help (AWH) – a writing assistance tool to proofread text produced by users with dyslexia before they post on Facebook. AWH was powered by a neural machine translation (NMT) model that translates dyslexia style to non-dyslexia style writing. We evaluated the performance and the design of AWH through a week-long field study with 19 people with dyslexia and received highly positive feedback. Our field study demonstrated the value of providing better and more extensive writing support on SNSs, and the potential of AI for building a more inclusive Internet.
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, James Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Max Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim Hazelwood
Liqi Yan, Qifan Wang, Yiming Cu, Fuli Feng, Xiaojun Quan, Xiangyu Zhang, Dongfang Liu