Do you take notice of on-line restaurant reviews? Then here's some advice

When you read a restaurant review on TripAdvisor or its equivalent, look carefully at the date it was written. And if you come across a negative review and try and remember what the weather was like at the time. Why? Because a study of nearly 1.1 million reviews of 840,000 restaurants over nearly a decade shows that if the weather was uncomfortable the comments were more likely to be cruel than kind.

The researchers, Saeideh Bakhshi, a doctoral student at the Georgia Institute of Technology; her husband, Partha Kanuparthy, who works for Yahoo Labs; and Eric Gilbert, an assistant professor at the university, found that the most negative reviews were written when it was colder than 4C degrees or warmer than 38C (40 and 100 degrees on the fahrenheit scale), or if it was raining or snowing.

Ms. Bakhshi will present her findings next week in Seoul, South Korea, at the International World Wide Web Conference and in an interview with the New York Times said that some regions of the United States were more prolific in their online reviewing habits than others. Restaurants in the Northeast and on the West Coast were reviewed more than those in the South or the Midwest. Predictably, urban areas with a higher level of education and income tended to participate in online review sites more often.

Among the other findings:

People who waited a long time for a table in busy cities were more forgiving than those who waited in smaller communities. And sushi restaurants were consistently rated higher than hamburger places, the researchers said, showing that ambience and a higher meal cost could produce better reviews.

“That speaks to the perception of price,” Ms. Bakhshi said. “Places that have nice ambience and are listed as romantic or trendy or more expensive, the rating is higher.”

… The team found that Seattle, as a whole, tended to offer lower reviews than many other cities. Sunny San Diego had the most five-star reviews.

To reach their conclusions the authors created analytic computer models based on data from several sites, including TripAdvisor, Foursquare and Citysearch.