Action Trigger Specificity and Its Impact on Information Retrieval by Social Media Bots

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.