A persuasive approach to designing interactive tools around the promises and perils of social platforms

Every day, people interact with various social platforms. Diverse forms of social platforms opened up a plethora of data to study information dissemination and understanding crowd behavior in finer details. However, there is a flip side to this. People do not only get benefited by using social platforms; rather these platforms can also be exploited for spreading organized disinformation and unintended misinformation to a large audience. These social platforms, with access to the history of users' socio-political biases, can emerge as tools to shape mass opinion. Such a broad spectrum of diversity raises questions about how we can identify the promises and perils of social platforms and how we can design user-centric tools around them. Efficient identification of such promises and perils of social computing systems will require a convergence of social science, behavioral psychology, and persuasion theory with computing. My research shows ways to this convergence. In my dissertation, I have taken a theoretical approach to explain the existing structures of social platforms. My findings helped me to develop interactive tools for masses leveraging socio-political and psychological cues from the crowd. My work is empirical in nature, for which I drew intuitions from theories in social science and used a combination of qualitative and quantitative data analysis techniques to extract insights on users' behavior. My research leads to practical systems for human-centric applications and to this end, I chose a specific type of social platform: crowdfunding platform. Specifically, this dissertation makes three contributions. First, it investigates how different forms of crowdfunding platforms become promising resources in our daily life. To this end, I present two work. The first work demonstrates how scientific crowdfunding platforms assist young researchers to seek funding for their research projects through expert endorsements. The second work focuses on the novice entrepreneurs and explains how enterprise crowdfunding platforms assist novices to gather funding from the crowd for their creative ideas and how persuasive promotional videos are essential for those campaigns to be successful. The findings of this work led to the next part of this dissertation where I designed and built VidLyz, an interactive online tool, that can explain the significance and implication of persuasion factors to novice entrepreneurs who have no formal training in advertising and media studies. A follow-up user study showed that VidLyz can effectively guide novices step-by-step to make a concrete plan for their campaign videos. Finally, I take a step further and investigates the flip side of social platforms: how social platforms can increase onion polarization on traditionally stigmatized topics such as equal rights for LGBTIQ people. I show that even after getting exposed to content both in favor of and against equal rights for LGBTIQ people simultaneously, users develop a more polarized opinion on the stigmatized issue after the exposure. In the last part, this dissertation shows promising ways to mitigate the effect of attitude polarization and in-group sensitization with the help of behavioral priming techniques. The findings of this dissertation present structured ways of uncovering the promises and perils of social platforms and shows how these aspects can be leveraged to build interactive socio-technical systems. Overall, it may be fair to see this dissertation as a step forward to design socio-technical systems based on the knowledge learned from the interaction of the users of social platforms

DolphinAtack: Inaudible Voice Commands

Speech recognition (SR) systems such as Siri or Google Now have become an increasingly popular human-computer interaction method, and have turned various systems into voice controllable systems(VCS). Prior work on attacking VCS shows that the hidden voice commands that are incomprehensible to people can control the systems. Hidden voice commands, though hidden, are nonetheless audible. In this work, we design a completely inaudible attack, DolphinAttack, that modulates voice commands on ultrasonic carriers (e.g., f > 20 kHz) to achieve inaudibility. By leveraging the nonlinearity of the microphone circuits, the modulated low frequency audio commands can be successfully demodulated, recovered, and more importantly interpreted by the speech recognition systems. We validate DolphinAttack on popular speech recognition systems, including Siri, Google Now, Samsung S Voice, Huawei HiVoice, Cortana and Alexa. By injecting a sequence of inaudible voice commands, we show a few proof-of-concept attacks, which include activating Siri to initiate a FaceTime call on iPhone, activating Google Now to switch the phone to the airplane mode, and even manipulating the navigation system in an Audi automobile. We propose hardware and software defense solutions. We validate that it is feasible to detect DolphinAttack by classifying the audios using supported vector machine (SVM), and suggest to re-design voice controllable systems to be resilient to inaudible voice command attacks.Comment: 15 pages, 17 figure

Interpretable Explanations for Probabilistic Inference in Markov Logic

Markov Logic Networks (MLNs) represent relational knowledge using a combination of first-order logic and probabilistic models. In this paper, we develop an approach to explain the results of probabilistic inference in MLNs. Unlike approaches such as LIME and SHAP that explain black-box classifiers, explaining M LN inference is harder since the data is interconnected. We develop an explanation framework that computes importance weights for MLN formulas based on their influence on the marginal likelihood. However, it turns out that computing these importance weights exactly is a hard problem and even approximate sampling methods are unreliable when the MLN is large resulting in non-interpretable explanations. Therefore, we develop an approach where we reduce the large MLN into simpler coalitions of formulas that approximately preserve relational dependencies and generate explanations based on these coalitions. We then weight explanations from different coalitions and combine them into a single explanation. Our experiments illustrate that our approach generates more interpretable explanations in several text processing problems as compared to other state-of-the-art methods

Fatal attraction: Identifying mobile devices through electromagnetic emissions

Smartphones are increasingly augmented with sensors for a variety of purposes. In this paper, we show how magnetic field emissions can be used to fingerprint smartphones. Previous work on identiication rely on speciic characteristics that vary with the settings and components available on a device. This limits the number of devices on which one approach is effective. By contrast, all electronic devices emit a magnetic ield which is accessible either through the API or measured through an external device. We conducted an in-the-wild study over four months and collected mobile sensor data from 175 devices. In our experiments we observed that the electromagnetic field measured by the magnetometer identifies devices with an accuracy of 98.9%. Furthermore, we show that even if the sensor was removed from the device or access to it was discontinued, identiication would still be possible from a secondary device in close proximity to the target. Our findings suggest that the magnetic field emitted by smartphones is unique and fingerprinting devices based on this feature can be performed without the knowledge or cooperation of users