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  Jonathan Perry

  Undergraduate Student

We present FluxFlow, an interactive visual analysis system for revealing and analyzing anomalous information spreading in social media. Every day, millions of messages are created, commented on, and shared by people on social media websites, such as Twitter and Facebook. This provides valuable data for researchers and practitioners in many application domains, such as marketing, to inform decision-making. Distilling valuable social signals from the huge crowd’s messages, however, is challenging, due to the heterogeneous and dynamic crowd behaviours. The challenge is rooted in data analysts’ capability of discerning the anomalous information behaviours, such as the spreading of rumours or misinformation, from the rest that are more conventional patterns, such as popular topics and newsworthy events, in a timely fashion. FluxFlow incorporates advanced machine learning algorithms to detect anomalies and offers a set of novel visualization designs for presenting the detected threads for deeper analysis. We evaluated FluxFlow with real datasets containing the Twitter feeds captured during significant events such as Hurricane Sandy. Through quantitative measurements of the algorithmic performance and qualitative interviews with domain experts, the results show that the back-end anomaly detection model is effective in identifying anomalous retweeting threads, and its front-end interactive visualizations are intuitive and useful for analysts to discover insights in data and comprehend the underlying analytical model.

First language (L1) influence errors are very frequent in English learners (L2), even more so when the learner’s proficiency level is higher (upper-intermediate/advanced). Our project aims to analyze errors made by learners from specific L1’s using learner corpora. Based on the analysis we want to focus on a specific type of error and research a way to identify it automatically in learners’ essays depending on their L1. This would allow us to implement an application that helps English as Second Language (ESL) students to identify and analyze their errors and to better understand the reasoning behind them, consequently improving the students’ English level.

About the EduApps initiative

EduApps is a suite of apps housed in an online environment that focuses on the health, well-being and development of one’s mind, body and community. Our research project titled, “There’s an App for That” is investigating the design process, development, implementation and evaluation of this suite of educational apps. Specifically, we are interested in helping students build confidence and competence in the cognitive, socio-emotional and physical domains. We are also interested in the impact a learning portal can have on students’ learning, teachers and the surrounding community. We hope that our research can build capacity for investigating and affecting innovation in formal and informal education settings in the use of digital technology. We have partnered with school boards and community organizations to develop and research the apps. More about each of the domains — their purpose, apps and related research can be found at http://eduapps.ca/.

Acknowledgements

Providing high-quality, personalized feedback on essays and documents can be exhausting and time-consuming. While teachers love the personal touch of handwritten notes, digital annotation tools often fall short, turning annotations into static marks or focusing too heavily on basic grammar rather than the writer’s actual intent.
Enter AnnotateGPT, it bridges the gap between the natural feel of pen-and-paper grading and the advanced capabilities of AI, transforming simple pen strokes into rich, context-aware feedback.

How It Works

AnnotateGPT turns handwritten marks into a collaborative conversation between the human reviewer and the AI. The process is simple:

1. Annotate: The user marks up a document using a digital pen, similar to the standard interface (e.g., circling a paragraph, highlighting a sentence, or scribbling a quick note).
2. AI Guesses the Purpose: When the user taps their handwritten mark, the AI analyzes the strokes and the underlying text to guess what the reviewer is trying to correct (e.g., “grammar,” “sentence structure,” or “organization”).
3. User Confirms: The user selects the correct purpose, guiding the AI on what to focus on.
4. AI Generates Feedback: The AI expands that simple pen stroke into detailed, constructive, and contextually relevant feedback throughout the rest of the document.

Why It Matters

In a study with novice teachers, AnnotateGPT proved to be a game-changer for evaluating student work:

• Saves Time: Reviewers can use quick annotations and let the AI do the heavy lifting of writing out the detailed explanation.
• Higher-Quality Feedback: Instead of just pointing out that a sentence is “awkward,” the AI explains why it’s awkward and suggests actionable fixes.
• Broader Coverage: The AI acts as a second set of eyes, catching bigger-picture issues like logical flow and organization that a hurried reviewer might miss.

Looking Beyond Education

While AnnotateGPT is an incredible tool for education, we envision a future where pen-based annotations become a universal way to interact with AI. Imagine using a digital pen to draw spatial constraints for AI image generation, sketch out UI designs, or even circle an object on a live camera feed to ask an AI questions about the physical world.
AnnotateGPT shows that human-AI collaboration doesn’t just have to happen through text, it can happen right at the tip of your pen.

Explore AnnotateGPT

Read our paper: https://doi.org/10.1145/3772318.3790867
Project Page: https://vialab.github.io/AnnotateGPT/
GitHub: https://github.com/vialab/AnnotateGPT

Video Presentation