The aim was to identify hate speech targeted at minorities and people in a vulnerable position.
During the municipal elections in spring 2017, a group of researchers and practitioners specialising in computer science, media and communication implemented a hate speech identification campaign with the help of an algorithm based on machine learning.
At the beginning of the campaign, the algorithm was taught to identify hate speech as diversely as possible, for example, based on the big data obtained from open chat groups. The algorithm learned to compare computationally what distinguishes a text that includes hate speech from a text that is not hate speech and to develop a categorisation system for hate speech. The algorithm was then used daily to screen all openly available content the candidates standing in the municipal elections had produced on Facebook and Twitter. The candidates’ account information were gathered using the material in the election machine of the Finnish Broadcasting Company Yle.
Source: News: Internet researchers harnessed the power of algorithm to find hate speech – Aalto University
When one creates an account to get access to an online service, the service provider requires certain details to identify users who want to access their accounts. There are existing methods that can be built into such services to protect users and their privacy under certain circumstances. However, many service providers choose to use other methods to collect as much information about us as possible, and so frequently threaten online user privacy.
Source: Digital services collect unnecessary personal information | Karlstad University
Should incentives in blockchain systems be a last resort? The developer of the Algorand proof-of-stake system thinks so, but other experts disagree.
“Can you say anything about incentives in Algorand?”
That question was directed to Silvio Micali, an MIT professor who had just delivered a keynote on his theoretical proof-of-stake (PoS) system at the Financial Cryptography and Data Security conference in Malta, yesterday. And the Turing-award winner’s answer set a few back on their heels.
“Incentives are the hardest thing to do,” Micali said.
In 30 years as a cryptographer, he had spent the last 10 working on just that issue.
Source: No Incentive? Algorand Blockchain Sparks Debate at Cryptography Event – CoinDesk
PrivacyStreams Helps Developers Create Privacy Friendly Apps
Decision To Share Personal Data Need Not Be All or Nothing
A smartphone app that uses the raw feed from a device’s microphone or accesses its contact list can raise red flags for a user concerned about privacy. In many cases, however, the app doesn’t need all the details that users find most sensitive.
Researchers at Carnegie Mellon and Peking universities have addressed this dilemma by creating a service, PrivacyStreams, that enables app developers to access the smartphone data they need for app functionality while assuring users that their private information isn’t being sold to an online marketer or otherwise revealed.
A sleep-monitoring app, for instance, might need to access the smartphone’s microphone, but only to register loudness, not to monitor conversations. An app developer could simply sample the microphone feed every minute or so, use software in the PrivacyStreams library to transform the raw data to loudness and then send just the loudness data back to the smartphone for use by the app.
Source: PrivacyStreams Helps Developers Create Privacy Friendly Apps | Carnegie Mellon School of Computer Science
The EU-funded AMBER (Enhanced Mobile Biometrics) project has done just this. The project has shown it can identify gender by breaking down gestures and analysing the way in which users swipe screens using multiple datasets. In a recently published paper the team detail the software and protocol used for data collection, the feature set extracted and subsequent machine learning analysis. The results of this exploratory analysis have confirmed the possibility of sex prediction from the swipe gesture data, obtaining an encouraging 78% accuracy rate using swipe gesture data from two different directions.
Source: European Commission : CORDIS : News and Events : How you handle your phone gives away more than you think
A study casts a light not just on gender bias, but also on how algorithms work.
The tennis finals of the United States Open are Saturday for the women and Sunday for the men. On the court, except for the number of sets, they all face the same rules. When they walk off the court, though, the game changes.
Source: Sexism and Shopping: Female Players Get Most of the Odd Questions at the U.S. Open – The New York Times
In this paper, we study the linking patterns and discussion topics of political bloggers. Our aim is to measure the degree of interaction between liberal and conservative blogs, and to uncover any differences in the structure of the two communities. Specifically, we analyze the posts of 40 “A-list” blogs over the period of two months preceding the U.S. Presidential Election of 2004, to study how often they referred to one another and to quantify the overlap in the topics they discussed, both within the liberal and conservative communities, and also across communities. We also study a single day snapshot of over 1,000 political blogs. This snapshot captures blogrolls (the list of links to other blogs frequently found in sidebars), and presents a more static picture of a broader blogosphere. Most significantly, we find differences in the behavior of liberal and conservative blogs, with conservative blogs linking to each other more frequently and in a denser pattern.