  Numerous methods have been developed to monitor the spread of negativity in
modern years by eliminating vulgar, offensive, and fierce comments from social
media platforms. However, there are relatively lesser amounts of study that
converges on embracing positivity, reinforcing supportive and reassuring
content in online forums. Consequently, we propose creating an English-Kannada
Hope speech dataset, KanHope and comparing several experiments to benchmark the
dataset. The dataset consists of 6,176 user-generated comments in code mixed
Kannada scraped from YouTube and manually annotated as bearing hope speech or
Not-hope speech. In addition, we introduce DC-BERT4HOPE, a dual-channel model
that uses the English translation of KanHope for additional training to promote
hope speech detection. The approach achieves a weighted F1-score of 0.756,
bettering other models. Henceforth, KanHope aims to instigate research in
Kannada while broadly promoting researchers to take a pragmatic approach
towards online content that encourages, positive, and supportive.
