Predicting Gaming Related Properties from Twitter Accounts
Authors: Maria Gorinova, Yoad Lewenberg, Yoram Bachrach, Alfredo Kalaitzis, Michael Fagan, Dean Carignan, Nitin Gautam
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |