Team 1
CH2 the rise of big crisis data- James
There are many types of data and uses for them. As we as a world evolve and become more aware of what data can offer us the first question that we have to ask ourselves is what truly is the quality of the data and is it helping. In this chapter they take a deeper look into how data is actually used in the big picutre.
Starting way back in 1937, when London launched the very first emergency number they recieved 1336 calls the first week, out of that 1073 were real emergiences, 171 just wanted to speak to the operator and 91 calls were jokes or fake calls. That breaks down to about 10% of calls during that first week to be fraudulent in nature. Continuing on this trend the UK shows that less than 25% which equals about 5 million fake calls a year. In New York 911 operators recieve around 10,000 fake calls A DAY. I wanted to highlight these points because with our technology and moving into using communication tools such as social media and hashtags, these are more ways to communicate yes, but these are also more ways to see the infiltration of fake data.
Overviewing what I took away from this is that despite all of the fake data and trickory that is out there, the use of data and the quality of it still shows to be effective, organizations are still using these methods to save lives, still using technolgy no matter how convoluded as a mechanism for communication. Studies will show both sides of Big Data and many may see an overwhelming side of negativity, but social media is very strong and still young in how its used, but overall shows its effectiveness day in and day out. This chapter brings up a lot of important questions about how social media and data are used, in what kind of specifics are they used and how this data can shed light on how we as a world respond to our needs.
CH7 Verifying Big Crisis Data Via Crowd Computing-Kirsten
The chapter opens with “I’m not a Gaddafi”. This is a reference to the misuse of data and its manipulation. A test of Libya Crisis mapping volunteers was used to “vet” volunteers utilizing crowd sourced information to map data sets. The point of this test was to ensure the “rogue volunteers would not sabotage the crisis map by deliberately adding false or misleading information”. The test itself was a vetting of the volunteers social media presence of all kinds to include, private & Business websites, LinkedIn, Facebook, Instagram, etc. The end result was there is no true conclusion on if the evaluation and testing was actually successful, however there was no mapping information found that was false.
Political opinion also plays a significant role throughout this chapter. There is a relatively brief discussion on the political “fight back” from the Kremlin in Russia during the 2011 Russian elections. A map on reports or sightings of ballot stuffing of lack of impartiality was established and ultimately taken down. “At the peak of operations the map displayed well over 5,000 detailed reports of election violations that covered the entire face of Mother Russia”. A State Television site in rebellion of the map claimed that information in the map was false and showed television commercials of people emailing and calling in false information. Shortly after that the head of the Russian Election committee submitted a legal complaint which resulted in a $1,000.00 dollar fine to the creators of the map.
The Chapter closes with the idea of being unable to truly identify if crowd sourced information is indeed being falsified or not, discussing how to be a digital Sherlock Holmes. This concept was the crowd correcting the posts of those that were putting out inaccurate information. Furthering this concept were Skype detectives, which proved to actually be affective. Once a “rumor was identified and determined false 2,000 people could share that information within their own networks in minutes…. In addition members of the skype group were able to ping their media contacts to quickly counter the further spread of the misinformation”.
CH8 verifying big crisis data via artificial intelligence-
Natural disasters are a cruel, awful reality. When they do occur, the last thing somebody or perhaps an organization wants to spend valuable time and resources on is to verify the credibiltity of information taken in via artificial intellignce. Verifying the source and the context of information on the intake is difficult and tedious. Traditional journalism tied with the use of ever-changing artificial intelligence can help mitigate these problems during difficult times. The emotional toll that can wreck on an idividual can be greatly under appreciated. Non-credible sources make this use of technology all too real.
Recent “fake news” in the forms of tweets or other social media outlets is becomming all too common. Using mechanisms to verify the validity of the messages can be done via artifical intelligence and machine learning. Some may not know how machine learning can play a vital role in this process. Machine learning is an use of artificial intelligence that allows systems the opportunity to routinely become smarter and improve from knowledge without being programmed for that specific task. The chapter refers to an earthquake in Chile and how the role of noncredible tweets played a signficance. Even the BBC needs the use of this type of technology.
The abiliy for essentially anyone to deem data original and sincere can be done. There is no need for a devastating natural disaster to strike. The combination of artificial intelligence and machine learning is powerful and does have everyday application. Identifying fake posts on social media can be done with a relatively high level of accuracy. We have certainly come a long way from that now infamous clip for the morning television talk show “Today” where the hosts crack jokes about just what exactly is email. No #mythbuster tag needed here.
References
- Meier, P. (2015). Digital humanitarians: how big data is changing the face of humanitarian response. Routledge. CH2 the rise of big crisis data pp 31-47
- Meier, P. (2015). Digital humanitarians: how big data is changing the face of humanitarian response. Routledge. CH7 verifying big crisis data via crowd computing
- Meier, P. (2015). Digital humanitarians: how big data is changing the face of humanitarian response. Routledge. CH8 verifying big crisis data via artificial intelligence