{"doi":"10.1109/dsaa.2018.00083","title":"A Methodological Template to Construct Ground Truth of Authentic and Fake Online Reviews","abstract":null,"journal":"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)","year":2018,"id":46579,"datarank":0.45106999617161697,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"self_citation_contribution":0.31191623125197543,"citation_network_contribution":0.13915376491964154,"self_endowment_contribution":0.31191623125197543,"citer_contribution":0.13915376491964154,"corpus_percentile":null,"corpus_rank":null,"citation_count":7,"citer_count":6,"citers_with_citation_signal":4,"citers_with_endowment":4,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":215823,"name":"Snehasish Banerjee","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"A Methodological Template to Construct Ground Truth of Authentic and Fake Online Reviews","abstract":"With the emergence of opinion spam, scholars in recent years have been investigating how to distinguish between authentic and fake online reviews. In this research area however, constructing ground truth has been a tricky problem. When labeled datasets of authentic and fake reviews are unavailable, it becomes impossible to systematically investigate differences between the two. In light of this problem, the goal of this paper is three-fold: (1) To review existing approaches of developing ground truth, (2) To present an improved methodological template to construct ground truth, and (3) To conduct a quality-check of the newly constructed ground truth. The existing approaches are dissected to identify several peculiarities. The new approach invests in mitigating pitfalls in the current approaches. In the newly constructed ground truth, authentic reviews were found to be not easily distinguishable from fake reviews. Finally, new research directions are identified with the hope that scholars would be able to stay ahead in their relentless race against spammers.","is_dataset_classified":null,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":null,"pmcid":null,"openalex_id":"https://openalex.org/W2905544777","authors":[],"funders":[],"total_grants":0,"fwci":1.61,"citation_percentile":0.88938194,"influential_citations":0,"citation_trend":[{"year":2019,"count":1},{"year":2020,"count":1},{"year":2021,"count":2},{"year":2022,"count":2},{"year":2025,"count":1}],"oa_status":"closed","license":null,"oa_locations":[{"url":"https://eprints.whiterose.ac.uk/134043/1/groung_truth_R1.pdf","host_type":"GREEN"},{"url":"http://xplorestaging.ieee.org/ielx7/8620128/8631391/08631446.pdf?arnumber=8631446","host_type":"publisher"},{"url":"https://doi.org/10.1109/dsaa.2018.00083","host_type":""},{"url":"https://orcid.org/0000-0001-6355-0470>","host_type":"repository"}],"fields_of_study":["Spam and Phishing Detection","Misinformation and Its Impacts","Hate Speech and Cyberbullying Detection","Computer Science"],"mesh_terms":[],"keywords":["Construct (python library)","Ground truth","Common ground","Computer science","Quality (philosophy)","Data science","Epistemology","Artificial intelligence","Psychology","Social psychology","Philosophy"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-11T23:54:33.830023Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}