Events

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An increasing number of people are relying on online social media platforms like Twitter and Facebook to consume news and information about the world around them. This change has led to a paradigm shift in the way news and information is exchanged in our society - from traditional mass media to online social media.

With the changing environment, it's essential to study the information consumption of social media users and to audit how automated algorithms (like search and recommendation systems) are modifying the information that social media users consume. In this thesis, we fulfill this high-level goal with a two-fold approach. First, we propose the concept of information diets as the composition of information produced or consumed. Next, we quantify the diversity and bias in the information diets that social media users consume via the three main consumption channels on social media platforms: (a) word of mouth channels that users curate for themselves by creating social links, (b) recommendations that platform providers give to the users, and (c) search systems that users use to find interesting information on these platforms. We measure the information diets of social media users along three different dimensions of topics, geographic sources, and political perspectives.

Our work is aimed at making social media users aware of the potential biases in their consumed diets, and at encouraging the development of novel mechanisms for mitigating the effects of these biases.

In this talk I shall outline a summary of our five year long initiative studying the temporal dynamics of various human language-like entities over the social media. Some of the topics that I plan to cover are (a)  how opinion conflicts could be effectively used for incivility detection in Twitter [CSCW 2018], (b) how word borrowings can be automatically identified from social signals [EMNLP 2017] and (c)  how hashtags in Twitter form compounds like natural language words (e.g., #Wikipedia+#Blackout=#WikipediaBlackout) that become way more popular than the individual constituent hashtags [CSCW 2016, Honorable Mention].

In the early 1990s cryptography went into a foundational crisis when efficient quantum algorithms were discovered which could break almost all public key encryption schemes known at the time. Since then, a enormous research effort has been invested into basing public key cryptography, and secure computation in general, on problems which are conjectured to be hard even for quantum computers. While this research program has been resoundingly successful, even leading up the way to cryptographic milestones such as fully homomorphic encryption, there are still important cryptographic primitives for which no post-quantum secure protocols are known. Until very recently, one such primitive was 2-message oblivious transfer, a fundamental primitive in the field of secure two- and multi-party computation. I will discuss a novel construction of this primitive from the Learning With Errors (LWE) assumption, a lattice-based problem which is known to be as hard as worst-case lattice problems and conjectured to be post-quantum secure. The security of our construction relies on a fundamental Fourier-analytic property of lattices, namely the transference principle: Either a lattice or its dual must have short vectors.