Some of the most egregious public policies are crafted on the meme of “protecting our children.” The most egregious one of inserting tracking capability into every digital device and into almost every digital file for the stated purposes of protecting our children against pornography – but with the unstated purpose of mandating the federal government criminalize, identify, track, and prosecute copyright infringers to benefit the Hollywood content creators and distributors. To this end we have seen hidden taxes applied to digital media, suppression or degradation of digital devices capable of copying copyrighted content, digital rights management systems that are capable of penetrating our digital systems and destroying the last vestiges of personal privacy, and the suppression of public domain content that is merely wrapped in an electronic wrapper; which if broken is a criminal act.
Now comes the federal government trying to automate the detection of “cyberbullying” on the internet … with zero legal protections that the system and its keywords cannot be repurposed to detect and track so-called “political hate speech” as part of the federal government’s unwanted intrusion into monitoring and controlling the internet.
Cyberbullying Detection Using Content and Social Network Analysis
This project aims to define new approaches for automatic detection of cyberbullying by integrating the relevant research in social sciences and computer science.
Cyberbullying is a critical social problem that occurs over a technical substrate. According to a recent National Crime Prevention Council report, more than 40% of teenagers in the US have reported being cyberbullied. This is especially worrying as the multiple studies have reported that the victims of cyberbullying often deal with psychiatric and psychosomatic disorders.
Specifically, this research will advance the state of the art in cyberbullying detection beyond textual analysis by also giving due attention to the social relationships in which these bullying messages are exchanged.
A higher accuracy at detection would allow for better mitigation of the cyberbullying phenomenon and may help improve the lives of thousands of victims who are cyberbullied each year.
The results of this research will also open doors to employing social intervention mechanisms to help prevent cyberbullying incidents in future.
The findings from this research will also validate and refine existing theories on cyberbullying and potentially advance the field by creating a wave of data-driven analysis of the phenomenon. The generated data set will be made available to the larger research community, thus enabling new findings that can help counter this social problem.
This research will define new approaches for automatic detection of cyberbullying and validate and refine social science theories related to cyberbullying.
To understand cyberbullying, experts in social science have focused on personality, social relationships, and psychological factors involving both the bully and the victim.
Recently computer science researchers have also developed automated methods to identify cyberbullying messages by text mining cyber conversations. However, focusing only on the textual content may lead to a piecemeal understanding of the phenomenon and a limited detection performance. Hence, this research investigates: (1) whether analyzing social network features surrounding the network can improve the accuracy of cyberbullying detection, and (2) whether the findings of social science research on cyberbullying obtained via surveys, ethnography, and interview-based methods hold true when tested via automated data analysis undertaken at a much bigger scale.
By analyzing the social relationship graph between users and deriving features such as number of friends, network embeddedness, and relationship centrality, the project will validate (and potentially refine) multiple theories in social science literature and assimilate those findings to create better cyberbullying detectors.
The project will yield new, comprehensive models and algorithms that can be used for cyberbullying detection in automated settings. <Source>
In short, this grant will enable researchers to build algorithms to search for specific dictionary-driven keywords and then map the relationships between those who send and receive keyword-detected messages on the internet. There being absolutely no difference between the methodology to detect cyberbullying and that to detect constitutionally-protected free speech.
There are few laws pertaining to the data-mining of social media and the combination and extension of these sources with data contained in private financial, medical, political, and advertising databases such as those readily accessible to the federal government. A government which has become increasingly intrusive in the lives of ordinary law-abiding citizens – mainly under the guise of detecting terrorist threats to the homeland, and proceeding with a degree of secrecy that does not appear to protect our Constitutional freedoms.
Bottom line …
It is time for people to become more aware of what the federal government in doing in our name and the extent to which they use memes like cyberbullying, race- or religion-based hate speech, or terrorist activities to pursue their own political agenda.
There is no doubt that the toxic partisan culture that is deeply embedded in today’s political bureaucracy is going to be the downfall of America – as the politicians pander to specific segments of the population in return for campaign contributions and voter support. How many people realize that enhancing penalties for so-called “hate speech” is unconstitutional as it lack specificity, is applied according to prosecutorial discretion and denies equal protection under the law to people who have been attacked while the attacker used racial epithets and they people who have simply been attacked for other criminal reasons. The law should be: if you are attacked – for whatever reason – you are punished appropriately. Similarly, affirmative action is unconstitutional as it promotes on class of citizens and disadvantages another.
There is also no doubt that a corrupt lying politician like Hillary Clinton would love to automate the detection of her enemies so she and her omnipresent surrogates can attack them using Alinsky’s Rules for Radicals, and possibly even official government agencies such as the IRS, EPA (Environmental Protection Agency), OSHA (Occupational Safety and Health Administration), FCC (Federal Communications Commission), and FEC. (Federal Elections Commission).
It is time to demand updated laws. Any politician, government employee, agent, informant, contractor, etc. who accesses private non-public personal information for political, professional, or profit purposes is guilty of a crime and shall be incarcerated without the possibility of early-release, parole, or any reduction in sentence. Any politician, official, etc. who uses a government agency to pursue a political agenda against a citizen of the United States for political, professional, or profit purposes is guilty of a crime …
You may wish to join the Electronic Frontier Foundation (www.eff.org) where they are trying to realign our laws from an analog to a digital world.
Be careful of what and whom you vote for.
"The object in life is not to be on the side of the majority, but to escape finding oneself in the ranks of the insane." -- Marcus Aurelius