Yet even without accessing back-end data, police departments can mine readily available information from user news feeds. Even public feeds are full of data that can make policing more effective, from posts about crimes in progress to damning evidence offered freely by criminals and even live videos of crimes. Police departments need a better, more ethical way of mining social media.
Far from a rejection of social mining, the article seeks to raise questions and offer recommendations for applying these tools to public safety in a way that respects civil rights and prioritizes resident benefits. When pursuing social mining, cities must think more carefully about privacy, free speech, and profiling. However, governments must walk a fine line between collecting useful information and undermining resident privacy. Kortz cited U.
In Jones , the police installed a GPS tracker on the car of Antoine Jones without a warrant and used information from the tracker to ultimately charge Jones with drug possession. For, while people might expect that others are watching as they take a right turn at an intersection, they certainly do not expect that others are monitoring their car day and night for a month. Cate on the other hand has a different understanding of the privacy protections around social media mining.
Indeed, a number of other Supreme Court cases do seem to contest the idea that a mosaic of public data can violate reasonable expectation of privacy. For example, in United States v. Garcia , the Court ruled that attaching a GPS tracker to the car of someone suspected of manufacturing methamphetamine did not violate Fourth Amendment privacy protections. However, in Garcia , the Court left open the question of privacy protections for broader surveillance efforts.
The Court chose to leave open the matter of whether monitoring residents en masse without any suspicion of a crime committed would violate reasonable expectation of privacy. The issue becomes more complex still when you consider the blurred lines between public and private information on social media.
While Garcia established that necessarily public information—like the location of a car—can be monitored over time in the presence of reasonable suspicion, what about information intended by Facebook users only for friends that GeoFeedia and Snaptrends have made it their goal to access? Even with reasonable suspicion, would gathering such information be acceptable under the Fourth Amendment? Case law has established that information volunteered to a third party loses its privacy protections, but scholars have argued that Facebook posts do not fall under this category.
Again, these questions about privacy protections around social media mining seem to lack any definitive legal answer. Cate however offered that this may not be particularly important. According to him, legal constraints are not the most compelling limitation when it comes to privacy.
For Cate, the strongest protections against privacy violations are social and political rather than legal. Governments should therefore structure their data collection efforts around the desires and needs of residents. Cities should readily reveal what data they are collecting, how, and what they are using it for, and create channels for public feedback. Cate even proposed that governments create an institutional review board to assess their data collection efforts. This way, cities could ensure that residents understand and have provided input on social mining programs before deploying them.
In fact, Cate's proposition that governments should ensure residents are comfortable with data mining initiatives is inherently linked with Kortz's objections on the grounds of reasonable expectation of privacy. For, ensuring transparency as well as public participation and education around data mining initiatives is a way of bringing expectations in line with the technological capacities possessed by governments.
If you were someone prone to use colorful language on social media, would government efforts to target potential criminals based on social posts push you to change the way you speak? According to Kortz, this is a critical question for governments with social monitoring strategies.
The chilling effect describes the result of an indirect discouragement of free speech. If severe enough, a chill may constitute a violation of First Amendment protections and therefore be unconstitutional. Whether the nature of government activities is unconstitutional itself revolves around a few sub-questions.
The first is the type of information gathered by government surveillance; gathering public information—like public social media posts—is traditionally permissible. The second is the scope of the intelligence gathering. While there are no definite standards for what scope of surveillance is acceptable, in Handschu v.
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And with social media being made up of a vast number of platforms, where do you even start? As the term itself describes, social media data mining is very much like the mining industry.
It is the process of extracting precious elements that are not apparent even more so not easily attainable. With studies proving social media effects on the purchase decisions of most consumers, such platforms have become effective tools in marketing and business strategies. This report proves the importance of gathering consumer data from social media, otherwise known as social media data mining. Its main goal is to collect data from different social media sources and find new information that may be hidden.
Data mining can help us understand large sets of data. There are two approaches when it comes to data mining—supervised and unsupervised approaches. These approaches provide algorithms that you can use in identifying the hidden patterns in your data. The supervised approach depends on the inferred knowledge from previous data.
Meanwhile, an unsupervised approach automatically characterizes data by classifying them into similar elements. One example of an unsupervised approach in data mining is clustering.
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In this technique, the algorithm learns from training data. It then automatically categorizes the newly found data into the distinct classes generated by the first set of the data that was previously gathered. The data collected from social media platforms can be useful in identifying social media trends and gaining new and valuable insights for your business.
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