| 摘要 |
We show how to compute useful and robust metrics for a Bulk Message Envelope. These metrics are based on whether recipients of messages read them, and if so, whether they click on any links in those messages, or perform other allowed actions, and optionally the time order in which they perform these actions. We call these metrics a MessageRank, and show how these can be used by a message provider, like an ISP, to give more information to recipients, who can then form ad hoc groups (transient social networks) to assess a common BME and its sender. Users can also use their collective decision making to classify incoming messages. A message provider can offer these as value added services, to increase its attractiveness to its users, relative to other message providers that do not do so. We also show how to detect spammer probe accounts. These are used by spammers on large message providers, to craft messages that can pass through the providers' antispam filters. Our methods involve finding the earliest instances of messages in a Bulk Message Envelope that is spam. Or the earlier instance of a message pointing to a spammer domain. We show how spammers can respond to this, assuming best play on their part. In turn, we define user styles (heuristics) based on user click stream behavior, that can be used to isolate the probe accounts. Our methods can increase the spammers' manual effort and monetary cost.
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