Operational signals: how project descriptions influence freelancer bidding in online labor markets

Purpose This study investigates how project-level textual and contextual characteristics influence the number of bids received in online labor markets (OLMs). Specifically, it examines whether features such as readability, length, sentiment, technical specificity, budget and contract type serve as signals that affect freelancer engagement. Design/methodology/approach Using a dataset of over 47,000 project listings, the study employs natural language processing (NLP) and machine learning techniques to extract structured variables from unstructured project descriptions. Multinomial logistic regression and ordinary least squares (OLS) models are used to evaluate the impact of these features on bid volume. Interaction effects between readability and sentiment, and between length and budget, are also examined. Findings The results show that more complex (i.e. less readable) and longer project descriptions are associated with higher bid volume. A positive emotional tone significantly increases freelancer engagement, and fixed-price contracts are more likely to attract bids than hourly ones. Technical specificity, while generally reducing the number of bids, may help filter for more qualified freelancers. Interaction effects indicate that the positive impact of sentiment is amplified when descriptions are easier to read, and that longer descriptions are more effective when the budget is larger. Originality/value This research contributes to the literature by introducing machine-learned textual features as behavioral signals in OLMs and highlighting the interplay between content quality and structural attributes. It offers practical guidance for clients and platforms seeking to improve project engagement and market efficiency.