Opportunities of Sensor Journalism
There are endless opportunities for sensor journalism. It can change a community by raising awareness of a problem, informing on a trend, or testing a theory. Through Patrick Herron’s presentation, we learned that the Mystic River is just one opportunity to research natural and man-made affects of water quality. By testing the contents of the water, we can see what materials are harmful or beneficial to drinking, swimming and animals.
Challenges of Sensor Journalism
Challenges in sensor journalism can provide limitations. As sensor and data journalism are such new fields, the methods are constantly evolving. As Lily Bui pointed out in her presentation, group sensoring can be both a very effective method of obtaining information and it can be extremely flawed. While it can be beneficial to conduct a large-scale survey where participants conduct their own tests or surveys independently, like many large-scale science experiments, it is impossible to create a control, or way to moderate every element of the information gathering process. One participant may record information as accurately as possible, but as the journalist, how do you know whether they are telling the truth or that they conducted their research accurately?
Promise of Storytelling
The method of collecting data with sensors doesn’t promise anything, but in many cases it can assure an in-depth analysis of a problem or trend. Even if the results are not conclusive, any data set can provide reason to keep researching, testing and questioning.
Pitfalls of Sensor Journalism
There are both technological and ethical pitfalls in sensor journalism. Technological difficulties can come in sensor journalism through the gadgets journalists build, whether it’s an electronic or computing device. Today, we rely very heavily on technology, but we need to be conscious of how it can often fail us. For example, using the coqui sensor in class, we had several complications building it and therefore, testing our water samples. This is why it important to always question results and have a back up method for testing. Even when the coqui sensors worked well and gave us accurate readings, it was necessary to send samples to a lab for more precise testing.
This is also important in order to compare and contrast. Just as context is incredibly important in sensor journalism, so is comparing evidence against similar or different evidence. If the frequency numbers from the lab were to come back significantly different than the coqui results, we would need to examine the reasons they were so far apart. Similarly, if the lab numbers were the same as or close to the coqui numbers, it would also be beneficial to question why they are similar and what differences in the two methods could have led to inaccuracy. For example, there are many ways the coqui sensor could make inaccurate readings. Since it is homemade, the wiring could have been wrong, or the connection from the probes to the water could have been weak or altered. Also, every device is different and needs to be gauged to set a standard measurement across all devices. Results could appear the same, but mathematically be worlds apart, if the coqui was gauged differently than the lab’s device.
Context contributes to the ethical pitfalls of sensor journalism. A data set or graphic means nothing without context and the more information a journalist provides, the wider the scope of the analysis. As Kate Crawford points out in her talk, “Hidden Biases of Big Data,” large numbers that appear to represent the general population or a majority can be the opposite in reality. This was the case for the pot-hole application invented for Boston, a sort of crowd sourcing method used to report bad roads and potential for reconstruction. However, this app was limited because it could only be used by those who owned smart phones. There was a direct correlation with certain neighborhoods with a lower socio-economic standing and not as much representation in pot-hole surveying. This could potentially mean that road construction would only happen in wealthy communities, where the majority of smart phones existed. This example shows that there can be very deep rooted consequences when one or several communities are underrepresented. Therefore, a journalist must be careful in what information they trust and they must analyze the whole picture, not only the information presented. A government or community can purposely leave out information or misrepresent it, so it is our job to identify those limitations and react to it by presenting any contradictory information.
These pitfalls can create one more, very important limitation – insufficient time. One of the biggest aspects of journalism is providing time sensitive information. It’s easy for time to get away with you when you’re so focused on testing and accurate results, but a journalist cannot ignore their deadline. Every sensor journalism project will require a newsworthy date to publish by in order for the information to be relevant to readers. In the end, this may mean
What does it take for journalists to tell stories with sensors?
I believe there are five important elements to telling a good story through sensor journalism – patience, curiosity, an open mind, questioning and timeliness.
Sensor journalism requires a lot of patience! From conducting research and testing data, to interviewing the right sources, telling a story through sensors can be time consuming and frustrating. It is important to stay calm and push forward, even at times when technology fails or data leads to a dead end.
Every journalist must have curiosity, but a sensor journalist needs a certain type of curiosity to tell their story in the best way possible. They must question every result, data set and device. They should also search for ways to improve their methods for next time.
Technology changes in an instant, so a journalist must be willing to try and learn new methods. New devices and applications are invented every day, so a data journalist must do their best to keep up and be aware of changes. From reading up on science journals and technology news, to talking to scientists and programmers, there are several ways to stay in the know in order to report the most accurate information.
In sensor journalism, one must ever assume something is the truth, even when test results appear to back a conclusion. The key to this is to always accompany your information with more questions. Stories based on data are never over. There will always be more information to gather, more subjects to test or survey, so a journalist must be transparent in their final conclusion. If they recognize a fault, loophole, or open end, they must identify it and explain to their readers why there are still questions to ask.
As I mentioned before, while data journalism may be its own sector within this industry, it is still journalism. Data journalists will always have the same requirements as every other journalist, including deadlines. No matter how in depth and accurate a data journalist aims to make their story, it still must be published in time to make sense to the community. If it isn’t presented to the public at the right time, what then does it accomplish?