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  "documentTitle": "Joyy Inc. (YY)",
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  "authorName": "Carson Block",
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  "sourceTypeLabel": "Short seller",
  "presentationDate": "2020-11-18 00:00:00",
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      "text": "We concluded that YY Live's gift revenue is approximately 90% fake using these three methods.",
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      "text": "Former Bigo Country Executives Stated the Majority of Users were Fake...57\nBigo's Spin on the Circular Economy Is Recycling Beans Through Broadcasters...57\nBigo PRC Revenues Appear to Be Mostly Fake, From A Derelict Audio Streaming App...59\nAppendix A: Definitions Related to our Analysis...64\nAppendix B: Notes on Data & Data Sources...65\nAppendix C: Internal Network Usage...68",
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      "text": "fake revenue percentage: 90%",
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      "text": "We analyzed and sampled YY user data and gift revenue in three ways to estimate the percentage of FUs on YY Live and their associated revenue. We concluded that YY Live's gift revenue is approximately 90% fake using these three methods.",
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      "text": "The first method was analyzing almost 100,000 Paying Users (“PUs”) that XHL tracks. We found that we could cleanly class almost half of YY gifts by value as coming from YY-associated Fake Users (“FUs”). We did this by taking a core group of FUs in the data set bearing YY IP addresses and tracking the IMEI sharing that radiated out from that group.",
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      "text": "There are two components to our data analysis and collection methodology. Using Google Chrome web developer tools, we were able to track up to 88 data points of each transaction, including PU name, PU YY ID, transaction time, gift name, gift unit price, gift ID, quantity of gifts, and name and YY ID of the recipient, PU IP address and device IMEI. The device IMEIs suggest to us that approximately half of YY Live gift revenues originated from YY's own servers. Device IMEIs also showed us how pervasive roundtripping of gifts is on YY Live. We used a third-party data analytics service owned by a YY",
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      "text": "However, we also found the remaining half of YY gift revenue to be largely fraudulent by performing granular investigative work to identify FUs. Our second FU identification method, which ran in this vein, consisted of randomly sampling 96 Wuhan-based PUs during the Chinese COVID-19 lockdown. We found that ~87.5% of these Wuhan PUs were apparent FUs. The third method, again granular, consisted of sampling 96 Modern Brothers PUs. As discussed above, the Modern Brothers sample indicated that ~97.9% were actually FUs.",
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      "text": "We further verified our 90% fake revenue estimate by checking the revenue that leading channel owners reported to the SAIC against YY's claims. We found an 85.9% discrepancy between the revenue YY asserted for the top five channel owners and the revenue in their China credit reports.",
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      "text": "3 Shared mobile devices are determined by the display of identical shared IMEIs in the data recorded collected from YY.\n4 YY ID: YY has 4 IDs for each performer: room ID, Channel ID, YY account ID and UID. The UID is used by XHL and YY's system and could not be changed by the performer. The Room ID, Channel ID and YY account ID could be changed as a result of leaving the original channel owner to join a new channel owner, etc. The UID cannot be changed by the performer. We use the UID for the identification of performers to be consistent with XHL data.",
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      "text": "Our Data Analysis and Sampling Indicate that ~90% of YY Live Revenue Is Fake",
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