Hate Content Detection via Novel Pre-Processing Sequencing and Ensemble Methods Article Swipe

PDF

Anusha Chhabra , Dinesh Kumar Vishwakarma ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2409.05134

Social media, particularly Twitter, has seen a significant increase in incidents like trolling and hate speech. Thus, identifying hate speech is the need of the hour. This paper introduces a computational framework to curb the hate content on the web. Specifically, this study presents an exhaustive study of pre-processing approaches by studying the impact of changing the sequence of text pre-processing operations for the identification of hate content. The best-performing pre-processing sequence, when implemented with popular classification approaches like Support Vector Machine, Random Forest, Decision Tree, Logistic Regression and K-Neighbor provides a considerable boost in performance. Additionally, the best pre-processing sequence is used in conjunction with different ensemble methods, such as bagging, boosting and stacking to improve the performance further. Three publicly available benchmark datasets (WZ-LS, DT, and FOUNTA), were used to evaluate the proposed approach for hate speech identification. The proposed approach achieves a maximum accuracy of 95.14% highlighting the effectiveness of the unique pre-processing approach along with an ensemble classifier.

Related Topics

Computer Science

Computational Biology

Biology

Mathematics

Mathematical Analysis

Concepts

Computer science Content (measure theory) Computational biology Biology Mathematics Mathematical analysis

Metadata

Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2409.05134
PDF: https://arxiv.org/pdf/2409.05134
OA Status: green
Cited By: 1
Related Works: 10
OpenAlex ID: https://openalex.org/W4403617560

All OpenAlex metadata

Raw OpenAlex JSON

OpenAlex ID: https://openalex.org/W4403617560

Canonical identifier for this work in OpenAlex
DOI: https://doi.org/10.48550/arxiv.2409.05134

Digital Object Identifier
Title: Hate Content Detection via Novel Pre-Processing Sequencing and Ensemble Methods

Work title
Type: preprint

OpenAlex work type
Language: en

Primary language
Publication year: 2024

Year of publication
Publication date: 2024-09-08

Full publication date if available
Authors: Anusha Chhabra, Dinesh Kumar Vishwakarma

List of authors in order
Landing page: https://arxiv.org/abs/2409.05134

Publisher landing page
PDF URL: https://arxiv.org/pdf/2409.05134

Direct link to full text PDF
Open access: Yes

Whether a free full text is available
OA status: green

Open access status per OpenAlex
OA URL: https://arxiv.org/pdf/2409.05134

Direct OA link when available
Concepts: Computer science, Content (measure theory), Computational biology, Biology, Mathematics, Mathematical analysis

Top concepts (fields/topics) attached by OpenAlex
Cited by: 1

Total citation count in OpenAlex
Citations by year (recent): 2025: 1

Per-year citation counts (last 5 years)
Related works (count): 10

Other works algorithmically related by OpenAlex

Full payload

id	https://openalex.org/W4403617560
doi	https://doi.org/10.48550/arxiv.2409.05134
ids.doi	https://doi.org/10.48550/arxiv.2409.05134
ids.openalex	https://openalex.org/W4403617560
fwci
type	preprint
title	Hate Content Detection via Novel Pre-Processing Sequencing and Ensemble Methods
biblio.issue
biblio.volume
biblio.last_page
biblio.first_page
topics[0].id	https://openalex.org/T12262
topics[0].field.id	https://openalex.org/fields/17
topics[0].field.display_name	Computer Science
topics[0].score	0.9983999729156494
topics[0].domain.id	https://openalex.org/domains/3
topics[0].domain.display_name	Physical Sciences
topics[0].subfield.id	https://openalex.org/subfields/1702
topics[0].subfield.display_name	Artificial Intelligence
topics[0].display_name	Hate Speech and Cyberbullying Detection
is_xpac	False
apc_list
apc_paid
concepts[0].id	https://openalex.org/C41008148
concepts[0].level	0
concepts[0].score	0.5534522533416748
concepts[0].wikidata	https://www.wikidata.org/wiki/Q21198
concepts[0].display_name	Computer science
concepts[1].id	https://openalex.org/C2778152352
concepts[1].level	2
concepts[1].score	0.4397461712360382
concepts[1].wikidata	https://www.wikidata.org/wiki/Q5165061
concepts[1].display_name	Content (measure theory)
concepts[2].id	https://openalex.org/C70721500
concepts[2].level	1
concepts[2].score	0.3863760530948639
concepts[2].wikidata	https://www.wikidata.org/wiki/Q177005
concepts[2].display_name	Computational biology
concepts[3].id	https://openalex.org/C86803240
concepts[3].level	0
concepts[3].score	0.19594618678092957
concepts[3].wikidata	https://www.wikidata.org/wiki/Q420
concepts[3].display_name	Biology
concepts[4].id	https://openalex.org/C33923547
concepts[4].level	0
concepts[4].score	0.14919644594192505
concepts[4].wikidata	https://www.wikidata.org/wiki/Q395
concepts[4].display_name	Mathematics
concepts[5].id	https://openalex.org/C134306372
concepts[5].level	1
concepts[5].score	0.0
concepts[5].wikidata	https://www.wikidata.org/wiki/Q7754
concepts[5].display_name	Mathematical analysis
keywords[0].id	https://openalex.org/keywords/computer-science
keywords[0].score	0.5534522533416748
keywords[0].display_name	Computer science
keywords[1].id	https://openalex.org/keywords/content
keywords[1].score	0.4397461712360382
keywords[1].display_name	Content (measure theory)
keywords[2].id	https://openalex.org/keywords/computational-biology
keywords[2].score	0.3863760530948639
keywords[2].display_name	Computational biology
keywords[3].id	https://openalex.org/keywords/biology
keywords[3].score	0.19594618678092957
keywords[3].display_name	Biology
keywords[4].id	https://openalex.org/keywords/mathematics
keywords[4].score	0.14919644594192505
keywords[4].display_name	Mathematics
language	en
locations[0].id	pmh:oai:arXiv.org:2409.05134
locations[0].is_oa	True
locations[0].source.id	https://openalex.org/S4306400194
locations[0].source.issn
locations[0].source.type	repository
locations[0].source.is_oa	True
locations[0].source.issn_l
locations[0].source.is_core	False
locations[0].source.is_in_doaj	False
locations[0].source.display_name	arXiv (Cornell University)
locations[0].source.host_organization	https://openalex.org/I205783295
locations[0].source.host_organization_name	Cornell University
locations[0].source.host_organization_lineage	https://openalex.org/I205783295
locations[0].license
locations[0].pdf_url	https://arxiv.org/pdf/2409.05134
locations[0].version	submittedVersion
locations[0].raw_type	text
locations[0].license_id
locations[0].is_accepted	False
locations[0].is_published	False
locations[0].raw_source_name
locations[0].landing_page_url	http://arxiv.org/abs/2409.05134
locations[1].id	doi:10.48550/arxiv.2409.05134
locations[1].is_oa	True
locations[1].source.id	https://openalex.org/S4306400194
locations[1].source.issn
locations[1].source.type	repository
locations[1].source.is_oa	True
locations[1].source.issn_l
locations[1].source.is_core	False
locations[1].source.is_in_doaj	False
locations[1].source.display_name	arXiv (Cornell University)
locations[1].source.host_organization	https://openalex.org/I205783295
locations[1].source.host_organization_name	Cornell University
locations[1].source.host_organization_lineage	https://openalex.org/I205783295
locations[1].license
locations[1].pdf_url
locations[1].version
locations[1].raw_type	article
locations[1].license_id
locations[1].is_accepted	False
locations[1].is_published
locations[1].raw_source_name
locations[1].landing_page_url	https://doi.org/10.48550/arxiv.2409.05134
indexed_in	arxiv, datacite
authorships[0].author.id	https://openalex.org/A5083748222
authorships[0].author.orcid
authorships[0].author.display_name	Anusha Chhabra
authorships[0].author_position	first
authorships[0].raw_author_name	Chhabra, Anusha
authorships[0].is_corresponding	False
authorships[1].author.id	https://openalex.org/A5021449557
authorships[1].author.orcid	https://orcid.org/0000-0002-1026-0047
authorships[1].author.display_name	Dinesh Kumar Vishwakarma
authorships[1].author_position	last
authorships[1].raw_author_name	Vishwakarma, Dinesh Kumar
authorships[1].is_corresponding	False
has_content.pdf	False
has_content.grobid_xml	False
is_paratext	False
open_access.is_oa	True
open_access.oa_url	https://arxiv.org/pdf/2409.05134
open_access.oa_status	green
open_access.any_repository_has_fulltext	False
created_date	2025-10-10T00:00:00
display_name	Hate Content Detection via Novel Pre-Processing Sequencing and Ensemble Methods
has_fulltext	False
is_retracted	False
updated_date	2025-11-06T06:51:31.235846
primary_topic.id	https://openalex.org/T12262
primary_topic.field.id	https://openalex.org/fields/17
primary_topic.field.display_name	Computer Science
primary_topic.score	0.9983999729156494
primary_topic.domain.id	https://openalex.org/domains/3
primary_topic.domain.display_name	Physical Sciences
primary_topic.subfield.id	https://openalex.org/subfields/1702
primary_topic.subfield.display_name	Artificial Intelligence
primary_topic.display_name	Hate Speech and Cyberbullying Detection
related_works	https://openalex.org/W4391375266, https://openalex.org/W2899084033, https://openalex.org/W2748952813, https://openalex.org/W2390279801, https://openalex.org/W4391913857, https://openalex.org/W2358668433, https://openalex.org/W4396701345, https://openalex.org/W2376932109, https://openalex.org/W2001405890, https://openalex.org/W4396696052
cited_by_count	1
counts_by_year[0].year	2025
counts_by_year[0].cited_by_count	1
locations_count	2
best_oa_location.id	pmh:oai:arXiv.org:2409.05134
best_oa_location.is_oa	True
best_oa_location.source.id	https://openalex.org/S4306400194
best_oa_location.source.issn
best_oa_location.source.type	repository
best_oa_location.source.is_oa	True
best_oa_location.source.issn_l
best_oa_location.source.is_core	False
best_oa_location.source.is_in_doaj	False
best_oa_location.source.display_name	arXiv (Cornell University)
best_oa_location.source.host_organization	https://openalex.org/I205783295
best_oa_location.source.host_organization_name	Cornell University
best_oa_location.source.host_organization_lineage	https://openalex.org/I205783295
best_oa_location.license
best_oa_location.pdf_url	https://arxiv.org/pdf/2409.05134
best_oa_location.version	submittedVersion
best_oa_location.raw_type	text
best_oa_location.license_id
best_oa_location.is_accepted	False
best_oa_location.is_published	False
best_oa_location.raw_source_name
best_oa_location.landing_page_url	http://arxiv.org/abs/2409.05134
primary_location.id	pmh:oai:arXiv.org:2409.05134
primary_location.is_oa	True
primary_location.source.id	https://openalex.org/S4306400194
primary_location.source.issn
primary_location.source.type	repository
primary_location.source.is_oa	True
primary_location.source.issn_l
primary_location.source.is_core	False
primary_location.source.is_in_doaj	False
primary_location.source.display_name	arXiv (Cornell University)
primary_location.source.host_organization	https://openalex.org/I205783295
primary_location.source.host_organization_name	Cornell University
primary_location.source.host_organization_lineage	https://openalex.org/I205783295
primary_location.license
primary_location.pdf_url	https://arxiv.org/pdf/2409.05134
primary_location.version	submittedVersion
primary_location.raw_type	text
primary_location.license_id
primary_location.is_accepted	False
primary_location.is_published	False
primary_location.raw_source_name
primary_location.landing_page_url	http://arxiv.org/abs/2409.05134
publication_date	2024-09-08
publication_year	2024
referenced_works_count	0
abstract_inverted_index.a	6, 29, 91, 144
abstract_inverted_index.an	44, 159
abstract_inverted_index.as	110
abstract_inverted_index.by	50
abstract_inverted_index.in	9, 94, 103
abstract_inverted_index.is	20, 101
abstract_inverted_index.of	23, 47, 54, 58, 65, 147, 152
abstract_inverted_index.on	37
abstract_inverted_index.to	32, 115, 131
abstract_inverted_index.DT,	126
abstract_inverted_index.The	68, 140
abstract_inverted_index.and	13, 88, 113, 127
abstract_inverted_index.for	62, 136
abstract_inverted_index.has	4
abstract_inverted_index.the	21, 24, 34, 38, 52, 56, 63, 97, 117, 133, 150, 153
abstract_inverted_index.This	26
abstract_inverted_index.best	98
abstract_inverted_index.curb	33
abstract_inverted_index.hate	14, 18, 35, 66, 137
abstract_inverted_index.like	11, 78
abstract_inverted_index.need	22
abstract_inverted_index.seen	5
abstract_inverted_index.such	109
abstract_inverted_index.text	59
abstract_inverted_index.this	41
abstract_inverted_index.used	102, 130
abstract_inverted_index.web.	39
abstract_inverted_index.were	129
abstract_inverted_index.when	72
abstract_inverted_index.with	74, 105, 158
abstract_inverted_index.Three	120
abstract_inverted_index.Thus,	16
abstract_inverted_index.Tree,	85
abstract_inverted_index.along	157
abstract_inverted_index.boost	93
abstract_inverted_index.hour.	25
abstract_inverted_index.paper	27
abstract_inverted_index.study	42, 46
abstract_inverted_index.95.14%	148
abstract_inverted_index.Random	82
abstract_inverted_index.Social	0
abstract_inverted_index.Vector	80
abstract_inverted_index.impact	53
abstract_inverted_index.media,	1
abstract_inverted_index.speech	19, 138
abstract_inverted_index.unique	154
abstract_inverted_index.(WZ-LS,	125
abstract_inverted_index.Forest,	83
abstract_inverted_index.Support	79
abstract_inverted_index.content	36
abstract_inverted_index.improve	116
abstract_inverted_index.maximum	145
abstract_inverted_index.popular	75
abstract_inverted_index.speech.	15
abstract_inverted_index.Decision	84
abstract_inverted_index.FOUNTA),	128
abstract_inverted_index.Logistic	86
abstract_inverted_index.Machine,	81
abstract_inverted_index.Twitter,	3
abstract_inverted_index.accuracy	146
abstract_inverted_index.achieves	143
abstract_inverted_index.approach	135, 142, 156
abstract_inverted_index.bagging,	111
abstract_inverted_index.boosting	112
abstract_inverted_index.changing	55
abstract_inverted_index.content.	67
abstract_inverted_index.datasets	124
abstract_inverted_index.ensemble	107, 160
abstract_inverted_index.evaluate	132
abstract_inverted_index.further.	119
abstract_inverted_index.increase	8
abstract_inverted_index.methods,	108
abstract_inverted_index.presents	43
abstract_inverted_index.proposed	134, 141
abstract_inverted_index.provides	90
abstract_inverted_index.publicly	121
abstract_inverted_index.sequence	57, 100
abstract_inverted_index.stacking	114
abstract_inverted_index.studying	51
abstract_inverted_index.trolling	12
abstract_inverted_index.available	122
abstract_inverted_index.benchmark	123
abstract_inverted_index.different	106
abstract_inverted_index.framework	31
abstract_inverted_index.incidents	10
abstract_inverted_index.sequence,	71
abstract_inverted_index.K-Neighbor	89
abstract_inverted_index.Regression	87
abstract_inverted_index.approaches	49, 77
abstract_inverted_index.exhaustive	45
abstract_inverted_index.introduces	28
abstract_inverted_index.operations	61
abstract_inverted_index.classifier.	161
abstract_inverted_index.conjunction	104
abstract_inverted_index.identifying	17
abstract_inverted_index.implemented	73
abstract_inverted_index.performance	118
abstract_inverted_index.significant	7
abstract_inverted_index.considerable	92
abstract_inverted_index.highlighting	149
abstract_inverted_index.particularly	2
abstract_inverted_index.performance.	95
abstract_inverted_index.Additionally,	96
abstract_inverted_index.Specifically,	40
abstract_inverted_index.computational	30
abstract_inverted_index.effectiveness	151
abstract_inverted_index.classification	76
abstract_inverted_index.identification	64
abstract_inverted_index.pre-processing	48, 60, 70, 99, 155
abstract_inverted_index.best-performing	69
abstract_inverted_index.identification.	139
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	2
citation_normalized_percentile