Once the algorithm was trained, a sample of about 1,000 words was enough to obtain a reasonably accurate classification of age and gender. Our results should be considered a starting point,” says the research team. “The fact that we have such a small data set and our predictions still work, suggests how much more could be done. But while the volume of information in the WhatsApp research is far less, its shortfall is compensated by the nature of the information and the more intimate way we express ourselves in these private messaging spaces. Similar studies using social networks as a source of content have based their analyses on large text samples of tens of millions of words contributed by tens of thousands of volunteers. After filtering out cases where age and gender were not provided and exchanges that were too brief, the researchers were left with 226 subjects, 309,229 messages and 1,949,518 words.
Koch and his team based their algorithm on the contents of What’s up, Deutschland?, a project that studied 451,938 WhatsApp conversations between 495 German volunteers. How many messages does it take to identify us? That depends on what part of the process we’re considering. “Facebook is shifting the focus to these conversations and they will probably want to use the data, so we need to have a conversation about how to protect those messages and make sure that, if they are labeled as private, they really are,” says Koch. Koch and his research team’s concerns come as social networks increasingly favor the use of private messaging spaces. You reveal more about yourself in private messages, not only in the content, but also in the way you use language Timo Koch, researcher at Munich University’s department of Psychology
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