This paper explored driving factors of herd information searching from the perspective of cognitive load in order to guide users to conduct rational herd information searching. Taking Weibo Hot Search as a case, grounded theory was employed to analyze and code original interview data in terms of three levels. During open coding, 219 initial concepts were obtained and 31 basic categories were elicited. During axial coding, 12 main categories were elicited. During selective coding, herd information searching was identified as the core category by combing the relationship structure among categories. A theoretical model about driving factors of herd information searching was developed. It is found that intrinsic cognitive load (information quality, perceived usefulness), extraneous cognitive load (source credibility, reputation, perceived ease of use), germane cognitive load (information needs, perceived information control, habit), perceived benefits, emotional experience and subjective norms have direct impacts on herd information searching.