Online Social Spammer Detection
Authors: Xia Hu, Jiliang Tang, Huan Liu
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on Twitter datasets confirm the effectiveness and efficiency of the proposed framework. |
| Researcher Affiliation | Academia | Xia Hu, Jiliang Tang, Huan Liu Computer Science and Engineering, Arizona State University, USA {xiahu, jiliang.tang, huan.liu}@asu.edu |
| Pseudocode | Yes | Algorithm 1: Online Social Spammer Detection |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the methodology described. |
| Open Datasets | Yes | TAMU Social Honeypots Dataset (Twitter T):1 This dataset was originally collected from December 30, 2009 to August 2, 2010 on Twitter and introduced in (Lee et al. 2011). Twitter Suspended Spammers Dataset (Twitter S): Following the data crawling process used in (Yang et al. 2011; Zhu et al. 2012), we crawled this Twitter dataset from July to September 2012 via the Twitter Search API. |
| Dataset Splits | Yes | In the experiments, five-fold cross-validation is used for all the methods. To study the effects brought by different sizes of training data, we varies the training data from 10% to 100%. In particular, for each round of the experiment, 20% of the dataset is held for testing and 10% to 100% of the original training data is sampled for training. |
| Hardware Specification | Yes | The experiments are run on a single-CPU, eight-core 3.40Ghz machine. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | One positive parameters is involved in the experiments. is to control the contribution of social network information. As a common practice, all the parameters can be tuned via cross-validation with validation data. In the experiments, we empirically set = 0.1 for experiments. |