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During the municipal elections in spring 2017, a group of researchers and practitioners specialising in computer science, media and communication implemented a hate speech identification campaign with the help of an algorithm based on machine learning.
At the beginning of the campaign, the algorithm was taught to identify hate speech as diversely as possible, for example, based on the big data obtained from open chat groups. The algorithm learned to compare computationally what distinguishes a text that includes hate speech from a text that is not hate speech and to develop a categorisation system for hate speech. The algorithm was then used daily to screen all openly available content the candidates standing in the municipal elections had produced on Facebook and Twitter. The candidates' account information were gathered using the material in the election machine of the Finnish Broadcasting Company Yle.
Parties - Hate - Speech - Election - Campaigns
All parties committed themselves to not accepting hate speech in their election campaigns. On the other hand, if the candidate used a personal Facebook profile instead of the page created and reported for the campaign, it was not included in the monitoring. Finnish word forms and the limited capability of the algorithm to interpret the context the same way humans do also proved to be challenging. The Perspective classifier developed by Google for the identification of hate speech has also suffered from the same problems in recognising the context and, for example, spelling mistakes.
Once the messages have been identified, it is key to define the actions that will follow.
Point - View - Authorities - Messages - Measures
"From the point of view of the authorities, there were no more than 20 messages that caused measures. Listing words as such is not sufficient because words get their meaning from the way they are combined. On the other hand, without the hate speech machine and researchers, we would not have the resources...
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