International Business Machines Corporation

Alexandre Rademaker

Ashish Kundu

Paulo Rodrigo Cavalin

Arjun Natarajan

Cicero Nogueira Dos Santos

Joshua F Payne

Maira Gatti De Bayser

Sanjana Sahayaraj

A method, computer system, and a computer program product for text bias identification and correction are provided. A first text corpus may be received. A designation of a second text corpus may be received. Words of the first text corpus may be embedded as a first word embedding in an embedding model. The first word embedding may be compared to a second word embedding in the embedding model to identify a first biased text in the first text corpus. The second word embedding may be from the second text corpus. A first replacement text portion may be generated as a substitute for the first biased text. The first replacement text portion may include a first unbiased text. The first biased text and the first replacement text portion may be presented.

Bias identification and correction in text documents

A computer-implemented method includes extracting a plurality of topics from a plurality of unlabeled social media posts, mapping the plurality of topics to a plurality of frequencies, each frequency in the plurality of frequencies indicating how often a corresponding topic in the plurality of topics occurs in the plurality of unlabeled social media posts, and predicting, based in part on the plurality of frequencies, a future social media posting behavior of a specific social media user, wherein the future social media posting behavior includes a specific topic about which the specific social media user is likely to post at a time in the future and a frequency with which the specific topic is likely to occur in posts of the specific social media user that are created at the time in the future.

Predicting user posting behavior in social media applications

Growing community of inventors

Rio de Janeiro, Brazil

Alexandre Rademaker