  Large language models~(LLMs) have demonstrated impressive performance in
various applications, among which role-playing language agents (RPLAs) have
engaged a broad user base. Now, there is a growing demand for RPLAs that
represent Key Opinion Leaders (KOLs), \ie, Internet celebrities who shape the
trends and opinions in their domains. However, research in this line remains
underexplored. In this paper, we hence introduce MINDECHO, a comprehensive
framework for the development and evaluation of KOL RPLAs. MINDECHO collects
KOL data from Internet video transcripts in various professional fields, and
synthesizes their conversations leveraging GPT-4. Then, the conversations and
the transcripts are used for individualized model training and inference-time
retrieval, respectively. Our evaluation covers both general dimensions (\ie,
knowledge and tones) and fan-centric dimensions for KOLs. Extensive experiments
validate the effectiveness of MINDECHO in developing and evaluating KOL RPLAs.
