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We propose an attention mechanism embedded in a quantile factor model, yielding information that is quantile\u2010specific, target\u2010specific, and horizon\u2010specific. We establish new asymptotic results and show empirically that targeted textual data improve quantile forecasts of exchange\u2010rate returns and industrial production growth relative to strong benchmarks and other conditional\u2010quantile models. Bigrams and trigrams drive these gains, extending evidence that collocations enhance forecasting. A portfolio\u2010management application yields better allocations and higher risk\u2010adjusted performance. Robustness checks include a synthetic\u2010misinformation stress test, which shows that fabricated news does not create spurious predictability. 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