Public Lab Research note


Sensor Journalism Reflection

by Jimmymcinnis | | 366 views | 0 comments |

Read more: publiclab.org/n/12732


Sensor Journalism is a growing sect of journalism that is creating a lot of buzz for both good and bad reasons. On one hand, it may hold the key to a more modern reporting, one where we rely heavily on data to find information as well as stories. On the other hand, it has proven to yield questionable results and often has issues with how it can present information. To clarify, Sensor journalism is a method of generating or collecting data from sensors, and then using that data to tell a story. As opposed to data-driven journalism, which requires finding datasets to report on, sensor journalism has the journalist collect the data his or herself. This allows for many new opportunities and possibilities in journalism, but also many drawbacks.

Sensor Journalism provides a lot of new opportunities for reporting and allows journalists to discover new stories and ways to find information for their stories. It can hold authority accountable in new ways, and it can help bridge the gap between the public and journalists, and make a more connected relationship.

One instance that exemplifies the creative new opportunities that sensor journalism can provide is the Sun Sentinel’s reporting of recklessly driving cops. Reporters in Miami were able to take data taken from toll bridge sensors to determine that many police officers were speeding on highways and creating reckless environments for the citizens. Previously, this was just something that had been reported anecdotally by citizens to the Miami newspaper, but this was nothing more than a “he said, she said” situation. By using data collected from sensors, this made it possible for journalists to confirm that the police were indeed speeding on highway roads.

During the 2008 Olympics the Chinese government claimed that they made the air quality better in Beijing better than it was, and good enough for athletes to participate in outdoor events. The Associated Press sent out a team of reporters with air quality sensors to go to various parts of the city during the Olympics and test the air quality. This type of journalism helped hold the Chinese government accountable.

In addition to the ability to hold authority figures accountable, whether it be the Miami Police Department or the Chinese government, Sensor Journalism also pairs well with citizen journalism, in that it allows the public to get involved in fact-finding and data collection. Weather apps like Weather Underground have users submit data from their own personal weather balloons as well as app inputs to help make the experience of data collection and representation more interactive. A more interactive and engaged audience is one that will stay more invested long-term, so Sensor Journalism has the ability to possibly fortify Journalism as a whole and its relationship with the public.

That being said, sensor journalism also has a serious amount of issues, making it a very difficult idea to rally around as journalists. For one, while sensors can focus on narrow, quantifiable data, they often lack the context in which that data fits in. To put this more clearly, in an experiment done to test the water conductivity of multiple samples, a sensor determined the differences in conductivity by emitting noise at a specific frequency correlating with the conductivity of the sample. In theory, this experiment shows how much extra material, or metals, are in the sample with the water molecules; the higher the frequency of the sensor means the higher conductivity in the sample meaning the more extra material in that sample.

However, this data is useless because it fails to recognize just what this “extra material” actually is. In the case of a water sample taken from a polluted river source, higher conductivity probably means it is dirtier than a standard or “control” clean water source. However, when a sample is taken from a mineral-infused water like Evian, the frequency is also higher. The minerals that Evian puts into its water are supposedly healthier for you. While the legitimacy of whether or not Evian is better than your tap is up for debate, it still does not change the fact that drinking it is not harmful. Yet Evian will have a high conductivity, and as a result a higher frequency.

It’s this grasp of greater context that makes it hard for sensor journalism to be treated as a feasible movement. What good is a conductivity reading if it can be a good thing in one sample and a bad thing in another? When given a mystery sample of water, how can one really tell whether or not the water is safe or contaminated?

Another big issue with sensor journalism is the concept of calibration. Sensors often have slight, minute differences between one another in their calibration. Two sensors supposedly testing the same sample may come up with completely different numbers if they have been calibrated differently.

This makes it near impossible to trust and compare the findings from sensors. How do we know that this reading is accurate? Additionally, comparing the findings between two sensors becomes near impossible. If sensor 1 finds a certain dataset from sample 1 and sensor 2 finds a certain dataset from sample 2, but sensor 2 tends to read lower values than sensor 1 when they test the same source, there really isn’t any way to compare the two datasets/samples fairly. Suddenly what was comparing apples to apples is now comparing apples to oranges.

One argument against this is to use one sensor for all samples to ensure that the samples are all tested equally. This might be a fine solution to a smaller scale test, but bigger, more complex projects like the Beijing Olympic air quality test could only be done with multiple sensors.

As a side note to the issue of calibration of the actual device or sensor, there is also potentially the issue of the human behind the sensor, and the chance for human error. It differs for every type of sensor, but potentially how the person uses and records data from the sensor will be different than another person. This could involve how diligently does the person use the sensor: are they meticulous or careless? It could also be their physical limitations: can they hold the sensor higher in the air to test air quality? Does that make a difference?

Some people may be excited and engaged in running tests, while others might only be mildly interested, and this can affect the quality as well as the frequency of the data collection. This inequality in tests can make datasets hard to compare as well. If some data is much more sparse than others in comparison, evaluating and analyzing the differences becomes much harder.

These pitfalls of sensor journalism make it hard to fully utilize at this point without questioning the validity/accuracy of the findings. But that doesn’t change the advent of new opportunities. It just means we need to be careful when deciding whether or not to use this method for fact-finding. Sensor journalism is a very dynamic field of journalism, and hopefully with more time and research it can eventually revolutionize how journalists find and obtain information to report.


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