Characterizing Information Shared by Participants to Coding Challenges: The Case of Advent of Code

Advent of Code (AoC from now on) is a popular coding challenge requiring to solve programming puzzles for a variety of skill sets and levels. AoC follows the advent calendar, therefore it is an annual challenge that lasts for 25 days. AoC participants usually post their solutions on social networks and discuss them online. These challenges are interesting to study since they could highlight the adoption of new tools, the evolution of the developer community, or the technological requirements of well-known companies. For these reasons, we first create a dataset of the 2019-2021 AoC editions containing the discussion threads made on the subreddit {\tt /r/adventofcode}. Then, we propose a model based on stream graphs to best study this context, where we represent its most important actors through time: participants, comments, and programming languages. Thanks to our model, we investigate user participation, adoption of new programming languages during a challenge and between two of them, and resiliency of programming languages based on a Stack Overflow survey. We find that the top-used programming languages are almost the same in the three years, pointing out their importance. Moreover, participants tend to keep the same programming language for the whole challenge, while the ones attending two AoCs usually change it in the next one. Finally, we observe interesting results about the programming languages that are ``Popular'' or ``Loved'' according to the Stack Overflow survey. Firstly, these are the ones adopted for the longest time in an AoC edition, thanks to which users have a high chance of reaching the end of the challenge. Secondly, they are the most chosen when a participant decides to change programming language during the same challenge.

Predicting Tweet Engagement with Graph Neural Networks

Social Networks represent one of the most important online sources to share content across a world-scale audience. In this context, predicting whether a post will have any impact in terms of engagement is of crucial importance to drive the profitable exploitation of these media. In the literature, several studies address this issue by leveraging direct features of the posts, typically related to the textual content and the user publishing it. In this paper, we argue that the rise of engagement is also related to another key component, which is the semantic connection among posts published by users in social media. Hence, we propose TweetGage, a Graph Neural Network solution to predict the user engagement based on a novel graph-based model that represents the relationships among posts. To validate our proposal, we focus on the Twitter platform and perform a thorough experimental campaign providing evidence of its quality.