Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Predicting Gaming Related Properties from Twitter Accounts
Authors: Maria Gorinova, Yoad Lewenberg, Yoram Bachrach, Alfredo Kalaitzis, Michael Fagan, Dean Carignan, Nitin Gautam
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present empirical results on the performance of our system based on its accuracy on our crowd-sourced dataset. |
| Researcher Affiliation | Collaboration | Maria Ivanova Gorinova University of Cambridge Cambridge, UK; Yoad Lewenberg The Hebrew University of Jerusalem Jerusalem, Israel; Yoram Bachrach Microsoft Research Cambridge, UK; Alfredo Kalaitzis Microsoft London, UK; Michael Fagan Microsoft London, UK; Dean Carignan Microsoft Redmond, US; Nitin Gautam Microsoft Redmond, US |
| Pseudocode | No | The paper describes the methodology in text but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology, nor does it include any repository links. |
| Open Datasets | No | The paper uses a custom dataset of '2,000 Twitter accounts, annotated by workers on Amazon s Mechanical Turk.' It does not provide any link, DOI, or citation for public access to this dataset. |
| Dataset Splits | Yes | Table 1 shows the accuracy of our predictions (measured using 10-fold cross validation). |
| Hardware Specification | No | The paper does not specify any hardware details like CPU, GPU models, or cloud instance types used for experiments. |
| Software Dependencies | No | The paper mentions 'Twitter API' and 'Microsoft Translator API' but does not specify software dependencies with version numbers (e.g., Python, specific ML libraries, OS versions). |
| Experiment Setup | No | The paper describes feature extraction and the use of linear/logistic regression, but does not provide specific hyperparameters (e.g., learning rate, batch size) or other detailed training configurations. |