arXiv:2610.28175v2 Announce Type: replace-cross Abstract: Social media sentiment is increasingly used to predict stock returns. Its reliability depends on the composition of the crowd generating the feed. For foreign firms, the home-market investors who know them best are underrepresented on English-language Twitter. Hence, their feed should carry a less precise signal. Reduced-form analysis of firm-level Twitter sentiment shows that sentiment noise predicts next-quarter losses less consistently for foreign stocks than for domestic stocks. The loss-on-noise slope is negative in 78 percent of quarters for domestic stocks but in only 48 percent for foreign stocks, with a significant mean difference in slopes. We interpret this difference through a noisy rational expectations model in which the feed carries a public signal produced by three types of contributors. Specialists post repeatedly with information about the firm, attention-driven contributors post once with information about the firm and a transient fad, and automated accounts post repeatedly with no information about the firm. The share of each contributor type and the size of the fad determine the precision of the signal, which, in turn, determines how much of the feed's noise reaches prices. The model gives repeat-author share, fad and precision from the vendor's aggregate metrics, without account-level data, whereas prior work backs out precision from price informativeness. The structural estimates show that feeds for foreign stocks have a higher share of repeat authors, a larger fad component and a less precise signal. Thus, the foreign signal did not disappear but got diluted.