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Your mobile phone records can tell if you are illiterate

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CIOL Your mobile phone records can tell if you are illiterate

Relevant data is crucial in tackling the menace of illiteracy, especially in developing nations, where statistics are often outdated. To aid that, Pål Sundsøy at Telenor Group Research in Fornebu, Norway has found a machine learning algorithm to tell whether individuals can read or write by analyzing their mobile phone records.

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There are over 750 million people the world over who are unable to read and write, two-thirds of which are women. One of the millennium development goals of the United Nations is to eradicate extreme poverty by 2030. One of the major contributing factors is illiteracy.

CIOL Your mobile phone records can tell if you are illiterate

The machine learning algorithm has found several factors that seem to predict illiteracy. The most powerful are the location where people spend most of their time. “One explanation can be that the model catches regions of low economic development status, e.g. slum areas where illiteracy is high,” says Sundsøy.

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Another predictor of illiteracy is the number of incoming texts and how they differ from the number of outgoing texts. That could be because people do not send texts to others who they know are illiterate, points out Sundsøy.

“By deriving economic, social, and mobility features for each mobile user we predict individual illiteracy status with 70 percent accuracy,” he says, pointing out that this allows areas with low literacy rates to be mapped.

The data can help aid agencies who allocate resources to areas with low literacy rates. It is important to map the areas with maximum illiteracy because it has several lifestyle impacts. Illiteracy breeds unemployed, poor health and leads them to be dependent on social welfare or charity.

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Although survey methods are available to determine literacy rates in countries, it is a time-consuming and expensive work. This algorithm provides a faster and cheaper way of mapping literacy rates.

The method uses straightforward number crunching. Sundsøy starts with a standard household survey of 76,000 mobile phone users living in an unidentified developing country in Asia. The survey was carried out by a mobile phone operator by a professional agency and logs each person’s mobile phone number and whether or not they can read.

This data is then matched with call data records from the mobile phone company. This provides data such as the numbers each person has called or texted, the length of these calls, air time purchases, cell tower locations, and so on.

Data on where all the individuals were when they made their calls or texts, who they were calling or texting, the number of texts received, at what time of day, can also be assessed through the data. Sundsøy used 75 percent of the data to search for patterns associated with users who are illiterate, using a variety of number crunching and machine learning techniques. He used the remaining 25 percent to test whether it is possible to use these patterns to identify illiterate people and areas where there is a higher proportion of illiterate people.

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