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Online labor platforms, such as the Amazon Mechanical Turk, provide aneffective framework for eliciting responses to judgment tasks. Previous workhas shown that workers respond best to financial incentives, especially toextra bonuses. However, most of the tested incentives involve describing thebonus conditions in formulas instead of plain English. We believe thatdifferent incentives given in English (or in qualitative framing) will resultin differences in workers' performance, especially when task difficulties vary.In this paper, we report the preliminary results of a crowdsourcing experimentcomparing workers' performance using only qualitative framings of financialincentives. Our results demonstrate a significant increase in workers'performance using a specific well-formulated qualitative framing inspired bythe Peer Truth Serum. This positive effect is observed only when the difficultyof the task is high, while when the task is easy there is no difference ofwhich incentives to use.

Qualitative Framing of Financial Incentives - A Case of Emotion Annotation