Fine-grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning
Authors: Tiziano Fagni, Stefano Cresci
JAIR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated our technique in two challenging classification tasks and we compared it to baselines and other state-of-the-art approaches. Our technique obtains the best results among all unsupervised techniques, with micro F1 = 0.426 in the 8-class task and micro F1 = 0.772 in the 3-class task. |
| Researcher Affiliation | Academia | Tiziano Fagni EMAIL Stefano Cresci EMAIL Institute of Informatics and Telematics (IIT) National Research Council (CNR) via G. Moruzzi 1, 56124 Pisa, Italy |
| Pseudocode | No | The paper describes the methodology using high-level overview diagrams (Figure 1, Figure 3) and lists steps textually (e.g., Section 5, steps for clustering). It does not contain structured pseudocode or algorithm blocks with code-like formatting. |
| Open Source Code | No | The paper states that data is publicly available, but there is no explicit statement about open-source code for the methodology or a link to a code repository. 'Our data are publicly available for scientific purposes5. 5. https://doi.org/10.5281/zenodo.5793346' |
| Open Datasets | Yes | Our data are publicly available for scientific purposes5. 5. https://doi.org/10.5281/zenodo.5793346 |
| Dataset Splits | Yes | Finally, we performed a stratified sampling to split our dataset into a training (90% 18,169 users), a validation (3% 604 users) and a test (7% 1,426 users) partition. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or processor types used for running the experiments. |
| Software Dependencies | No | The paper mentions 'sklearn Python software package' and 'gensim library', and 'UMAP with default parameters' but does not specify version numbers for these software components. '13. https://scikit-learn.org/stable/' '12. https://radimrehurek.com/gensim/' |
| Experiment Setup | Yes | In this work, we fixed k = 5 in Equation (3)... Th = 0.5 is a reasonable value... We leveraged UMAP with default parameters... we assume that we know the number of clusters we want to obtain at the end of clustering process (i.e., 8 clusters for the party prediction task and 3 clusters for pole prediction task)... Parties + clustering: ... step 2 with a feature reduction to 64 features, and step 4 using Gaussian Mixture with default parameters... Parties enriched + clustering: ... clustering process for the party prediction task is performed by applying only step 3 and step 4 using KMeans as the clustering algorithm. For the pole prediction task, we used instead step 1, step 2 with a feature reduction to 64 features, and step 4 using the KMeans algorithm. |