Action Trigger Specificity and Its Impact on Information Retrieval by Social Media Bots
Carolina Salge et al.
What the paper says
Organizations increasingly rely on social media bots for real-time monitoring. Yet, configuring bots for effective information retrieval remains challenging. Too much data creates noise; too little risks missing insights. We address this tradeoff by examining how action triggers—the search terms bots use—shape retrieval outcomes. We introduce volume-adjusted relevance, which weights relevance against retrieved volume and explore three design dimensions: semiotic specificity (hashtags vs. no-hashtags), semantic specificity (hypernyms vs. hyponyms), and trigger expansion (single vs. paired terms). In a large-scale randomized field experiment on X, a custom-built master bot retrieved over 8 million posts using 204 triggers across 50 objectives for one week. Results show that hashtags improve volume-adjusted relevance, semantic specificity provides limited benefit, and combining semantically related hashtags yields the best performance. These findings advance understanding of bot-based retrieval and offer a framework for reducing noise, avoiding blind spots, and enhancing social media monitoring.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.