Internet censorship is accomplished through technological means, it is sometimes enforced via automated keyword filtering with a keyword list, implemented on a platform or state level. Individual users who discuss “sensitive” issues online may find their social media posts are made invisible or removed and may even have their accounts shut down. In this project, I experimented with using images as carrier of text data to send sensitive inforamtion over a censored network and made a publicly available data-hiding web tool for this project. A PRNG-based, spatial domain embedding method was used to embed the text data, the carrier image is then sent over a network where a keyword-based censor was detected. The result of the experiment shows that steganography can be used to combat keyword-based internet censorship, which promotes the free exchange of information. 

Diaries can serve as a rich resource to understand the context of a time and place. The diaries of Joseph Mathia Svoboda capture over 40 years of trade on the Tigris, detailing his day to day journeys as a steamboat purser during the late 19th and early 20th centuries, specifically between the cities of Basra and Baghdad. These diaries offer a unique perspective into daily life, community structure, and social relations in the area, which are instrumental to understanding the intricate geopolitical relationship between the British and the Ottoman empires. However, with over 600 pages of transcribed material and many more diaries still in the process of being transcribed, it is difficult to track patterns and changes in Joseph Svoboda’s social relationships and daily life by way of reading and inference alone. This paper describes a project to automatically identify mentions of persons, explore social relationships in the Svoboda diaries, and analyze social networks over time through qualitative and quantitative metrics, to facilitate discussion of Ottoman Iraq society. To automatically extract people from the diaries, we use natural language processing (NLP) methods, and to explore social relationships, we employ network visualization techniques to render networks of persons mentioned in the diaries. Furthermore, by utilizing information gathered through close readings of the diaries and manual extraction, we render networks highlighting kinship and spatiality and then analyze these networks by identifying changes in prominent persons and changes in computed network metrics over time. This project combines the methods of natural language processing and social network analysis to show how the spatial and kinship relations of Joseph Mathia Svoboda change over time. Our research demonstrates how findings based on this methodology can facilitate the study of social and economic life in Ottoman Iraq.

Misinformation and disinformation online — and on social media in particular — have become a topic of widespread concern. Recently, Facebook and Social Science One released a large, unique, privacy-preserving dataset to researchers that contains data on user interactions with URLs shared on Facebook, including how users interact with posts and demographic data from those users. We analyzed this data through the lens of U.S. mis/disinformation and cross-referenced three lists of domains: Third-Party Fact Checked URLs marked within the dataset, 2016 Election Fake News sites from Grinberg et al. (2019), and Broad Misinformation domains from Zeng et al. (2020). We calculated average interaction by user demographic group for these URL subsets, finding distributions for views, clicks, and shares among other metrics (taking into account the differential privacy noise added to the data). Furthermore, we compared interaction metrics between the three mis/disinformation subsets and baseline U.S. URLs, finding that posts containing mis/disinformation URLs draw significant user engagement. We also find that older and more politically conservative U.S. users are more likely to be exposed to (and ultimately re-share) potential mis/disinformation, but that those users who are exposed are roughly equally likely to click. We discuss the implications of our findings for platform interventions and further study towards reducing the spread of mis/disinformation on social media.

With the rise of social media and video sharing platforms, many people are turning to sources like Youtube as their main source of information. However, these platforms have become easy targets for misinformation campaigns. Credibility is hard to ascertain on a video sharing platform like Youtube come from the wide base of content creators. On a typical Google search for a topic, the leading sources are usually mainstream-media companies. However, on Youtube, it is significantly easier for an individual person, or channel, to overtake mainstream media and become popular. There is also no standard way for creators to display credibility factors or cite their sources on video-sharing platforms. This leads to creators using a workaround to cite sources or simply foregoing citations all together. The implications of this for viewers is that they are unable to quickly identify the credibility of the source without cross-referencing other places on the web, or lateral reading. Currently, the only standardized information displayed about a channel is its display name, profile picture, subscriber count, and occasionally a verification indicator. The verification indicator, in particular, can be misleading as it only indicates whether a channel is who they claim to be. This project is researching and developing citations for Youtube videos. Video citations will allow creators to display credibility within individual videos and allow viewers to conduct lateral reading with ease. Citations specific to this format could also take on advantages of social media platforms, specifically user ratings and collaborative creation of citations. There are many possibilities in how citations can be introduced that we will explore through this project, each with pros and cons. For instance, sharing and creating credible videos could contribute to the pre-existing communities currently on Youtube through crowd-sourcing citations.

 Motor neuroprosthetics are brain-computer interfaces (BCIs) that record neural signals from an individual, use a decoding algorithm to predict the individual's intended movement, and perform that movement using a prosthetic device. As the neural code for movements is still being uncovered, current devices are still limited in their ability to create naturalistic movements. Development of motor decoding algorithms that can predict more naturalistic movement may enhance these prosthetics. In this project, we aim to implement and elaborate on newly published motor decoding algorithms and then compare alternative approaches to predicting the direction of movement in upper body joints (initially just the wrist) based on electrocorticography (ECoG) recordings from the surface of the human brain. Our dataset uses neural data collected during one week of continuous ECoG recordings from four subjects undergoing chronic, in-patient seizure monitoring, and includes hours of naturalistic movement, previously mapped from concurrent video to provide joint tracking of the head, shoulder, elbow and wrist. Carrying out this study on existing ECoG recordings provides a basis for extending our decoding work to future, real-time neural recordings. We have identified electrodes that correspond to hand-related areas in motor and sensory cortices for our initial focus on motor decoding of wrist movement. The goal of our project is to explore motor decoding of dimensionally-reduced spectral features using various neural network architectures. This will allow for more accurate responses in motor neuroprosthetics since they rely on motor decoding of naturalistic movements.