Detecting personalities in social media content is an important application of personality psychology. Most early studies apply a coherent piece of writing to personality detection, but today, the challenge is to identify dominant personality traits from a series of short, noisy social media posts. To this end, recent studies have attempted to individually encode the deep semantics of posts, often using attention-based methods, and then relate them, or directly assemble them into graph structures. However, due to the inherently disjointed and noisy nature of social media content, constructing meaningful connections remains challenging. While such methods rely on well-defined relationships between posts, effectively capturing these connections in fragmented and sparse content is non-trivial, particularly under limited supervision or noisy input. To tackle this, we draw inspiration from the scanning reading technique—commonly recommended for efficiently processing large volumes of information—and propose an index attention mechanism as a solution. This mechanism leverages prior psycholinguistic knowledge as an “index” to guide attention, thereby enabling more effective information fusion across scattered semantic signals. Building on this idea, we introduce the Index Attention Network (IAN)—a novel framework designed to infer personality labels by performing targeted information fusion over deep semantic representations of individual posts. Through a series of experiments, IAN achieved state-of-the-art performance on the Kaggle dataset and performance comparable to graph convolutional networks (GCN) on the Pandora dataset. Notably, IAN delivered an average improvement of 13% in terms of macro-F1 scores with the Kaggle dataset. The code for IAN is available at GitHub: https://github.com/Once2gain/IAN.