The study, presented at the Association for the Advancement of Artificial Intelligence Conference on Web and Social Media, analysed 532 million tweets published between 2011 and 2016.
"We found that more targeted, discriminatory tweets posted in a city related to a higher number of hate crimes. This trend across different types of cities (urban, rural, large, and small) confirms the need to more specifically study how different types of discriminatory speech online may contribute to consequences in the physical world," said Rumi Chunara, Assistant Professor at the New York University.
For the study, researchers with the help of Artificial Intelligence identified and analyzed two types of tweets: those that are targeted directly espousing discriminatory views and those that are self-narrative describing or commenting upon discriminatory remarks or acts.
The team compared the prevalence of each type of discriminatory tweet to the number of actual hate crimes reported during that same time period in those same cities.
The analysis included cities with a wide range of urbanisation, varying degrees of population diversity, and different levels of social media usage.
The team limited the dataset to tweets and bias crimes describing or motivated by race, ethnic or national origin-based discrimination.
According to the study, these insights could prove useful in identifying groups that may be likelier targets of racially motivated crimes and types of discrimination in different places.
While most tweets included in this analysis were generated by actual Twitter users, the team found that an average of eight per cent of tweets containing targeted discriminatory language was generated by bots.
( With inputs from IANS )