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..
Short Text Representation for Detecting Churn in Microblogs
Authors: Hadi Amiri, Hal Daume III
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on Twitter data about three telco brands show the utility of our approach for this task. |
| Researcher Affiliation | Academia | Hadi Amiri and Hal Daum e III Computational Linguistics and Information Processing (CLIP) Lab Institute for Advanced Computer Studies University of Maryland EMAIL |
| Pseudocode | No | The paper provides mathematical equations for the RNN model but does not include any pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | We utilize churn data provided by (Amiri and Daume III 2015)3. The data was collected from twitter for three telecommunication brands: Verizon, T-Mobile, and AT&T. Footnote 3: www.umiacs.umd.edu/ hadi/ch Data/ |
| Dataset Splits | Yes | We performed all the experiments through 10-fold cross validation and used the twotailed paired t-test ρ < 0.01 for significance testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the "Vowpal Wabbit classification toolkit" but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | We use our development dataset to learn our RNN model for tweet representation. For this, we set the size of hidden layer to m = 128 in the experiments. We employ Vowpal Wabbit classification toolkit4 with all parameters set to their default values to perform the classification experiments. |