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Twitter used by illegal online pharmacies to pedal drugs

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New York: Illegal online pharmacies are using Twitter to promote prescription drug abuse of controlled substances.

“This study is the first to establish an empirical link between Twitter content and illicit online pharmacies which promote the illegal sale of drugs that have significant abuse potential,” said study co-author Timothy Mackey from University of California-San Diego.

“Our results are concerning, as the study found over 45,000 tweets that promoted drug abuse even though we only looked at a two-week period of tweets,” Mackey added.

According to the US Department of Health and Human Services (HHS) and National Institute on Drug Abuse (NIDA), young adults are the biggest abusers of prescription opioid pain relievers, ADHD stimulants, and anti-anxiety drugs.

The study conducted surveillance and content analysis of over two million tweets using the Twitter public streaming application programming interface and used assisted machine learning.

Tweets were analysed to determine if they discussed drug abuse issues, whether they favourably promoted drug abuse behaviour, and were also examined to see if they directly enabled the illegal access to controlled substances by online pharmacies.

Mackey said the study findings are extremely troubling.

“When we examined links included in a subset of the tweets that discussed drug abuse, we discovered that 76 percent of these tweets included a link to an online marketing affiliate advertising the sale of the controlled substance valium, a tranquillizer that is commonly abused,” he said.

Buying prescription drugs from illegal online pharmacies also endanger consumers financial information, putting them at risk of identity theft, according to experts.

The study was published in the Journal of Medical Internet Research (JMIR). (IANS) (image courtesy:wsjgadjets.ndtv.com)

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New algorithm may help locate fake Facebook and Twitter accounts

Using the meta-features, the researchers, constructed a generic classifier that can detect fake profiles

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Facebook and Twitter fake accounts can now be identified. Pixabay

Scientists have developed a new generic algorithm based on machine-learning to detect fake accounts on social network platforms including Facebook and Twitter, an advance with considerable potential for applications in the cyber-security arena.

“With recent disturbing news about failures to safeguard user privacy, and targeted use of social media to influence elections, rooting out fake users has never been of greater importance,” said lead researcher Dima Kagan from the Ben-Gurion University (BGU) in Israel.

The icon of Facebook.
Facebook has many fake profiles. Pixabay

The study showed that the algorithm is generic, and efficient both in revealing fake users and in disclosing the influential people in social networks. “Overall, the results demonstrated that in a real-life friendship scenario we can detect people who have the strongest friendship ties as well as malicious users, even on Twitter,” the researchers said.

Based on machine-learning algorithms, the new method, detailed in the journal Social Network Analysis and Mining, works on the assumption that fake accounts tend to establish improbable links to other users in the networks.

Also Read: Facebook Rolls Out Fact-Checking News In Karnataka

It constructs a link prediction classifier that can estimate, with high accuracy, the probability of a link existing between two users. It also generates a new set of meta-features based on the features created by the link prediction classifier.

Twitter to soon release Snapchat like feature. VOA
Fake accounts on Twitter will be identifie too. VOA

Using the meta-features, the researchers, constructed a generic classifier that can detect fake profiles in a variety of online social networks. “We tested our algorithm on simulated and real-world data sets on 10 different social networks and it performed well on both,” Kagan said.

Previously, researchers from the BGU had developed the Social Privacy Protector (SPP) to help users evaluate their friends list in seconds to identify which have few or no mutual links and might be “fake” profiles. IANS

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