Sai MaUsing microdata from the Michigan Survey of Consumers, we study how within-household reallocations of attention across news affect inflation expectation bias, measured relative to a real-time, machine-learning full-information benchmark. Shifting attention toward unfavorable (favorable) economic news increases (decreases) forecast bias substantially, while dropping attention to an unfavorable topic has little effect. The largest bias increases come not from inflation news itself, but from attention to unfavorable social, political, and geopolitical narratives. Aggregate news sentiment has no effect on bias when a household's reported attention allocation is unchanged. In aggregate, these effects are amplified when the attention network is dominated by an unfavorable focal hub: bias-reducing favorable narratives are crowded out of limited attention sets, and respondents closer to the hub exhibit larger bias increases. We find that past and present attention to news together account for up to 70 percent of observed forecast bias, with the current attention component rising sharply during recessions and large negative news events. Results are robust to a battery of specification checks and external validation.
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