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筆者三個月前在本欄撰文，倡議「化西入中」的港式教育，吸引中國內地學生來港求學，從而促進香港和內地的人才流動，助推粵港澳大灣區經濟融合。業界一些朋友看了此文，頗感興趣，致電商討。一位朋友特別前來香港大學，跟筆者探討教育和人才問題。寒暄幾句後，他便單刀直入：「你提出的觀點，無非是把香港當作內地人才加工的基地，然後走內銷的道路。這對你們搞教育的當然是一盤好生意，但對香港又有什麼『着數』呢？」此問乍聽突兀，細想卻值得思辨一番。

筆者三個月前在本欄撰文，倡議「化西入中」的港式教育，吸引中國內地學生來港求學，從而促進香港和內地的人才流動，助推粵港澳大灣區經濟融合。業界一些朋友看了此文，頗感興趣，致電商討。一位朋友特別前來香港大學，跟筆者探討教育和人才問題。寒暄幾句後，他便單刀直入：「你提出的觀點，無非是把香港當作內地人才加工的基地，然後走內銷的道路。這對你們搞教育的當然是一盤好生意，但對香港又有什麼『着數』呢？」此問乍聽突兀，細想卻值得思辨一番。

去年夏天筆者剛從洛杉磯移居到香港這座闊別多年的城市，就接到所屬經管學院通知，因學生報名人數突然幾近翻倍，需多教一門應用計量經濟學的碩士課程。2019冠狀病毒病疫情下，香港特區的大學還能增產擴容，實屬難得。

去年夏天筆者剛從洛杉磯移居到香港這座闊別多年的城市，就接到所屬經管學院通知，因學生報名人數突然幾近翻倍，需多教一門應用計量經濟學的碩士課程。2019冠狀病毒病疫情下，香港特區的大學還能增產擴容，實屬難得。

This article addresses the challenges in classifying textual data obtained from open online platforms, which are vulnerable to distortion. Most existing classification methods minimize the overall classification error and may yield an undesirably large Type I error (relevant textual messages are classified as irrelevant), particularly when available data exhibit an asymmetry between relevant and irrelevant information. Data distortion exacerbates this situation and often leads to fallacious prediction. To deal with inestimable data distortion, we propose the use of the Neyman–Pearson (NP) classification paradigm, which minimizes Type II error under a user-specified Type I error constraint. Theoretically, we show that the NP oracle is unaffected by data distortion when the class conditional distributions remain the same. Empirically, we study a case of classifying posts about worker strikes obtained from a leading Chinese microblogging platform, which are frequently prone to extensive, unpredictable and inestimable censorship. We demonstrate that, even though the training and test data are susceptible to different distortion and therefore potentially follow different distributions, our proposed NP methods control the Type I error on test data at the targeted level. The methods and implementation pipeline proposed in our case study are applicable to many other problems involving data distortion. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.