Mining User Behavior Patterns in Online Social Networks: A Review of Recent Advances Article Swipe
This paper reviews the recent advances in mining user behavior patterns in online social networks, focusing on the integration of machine learning, deep learning, Natural Language Processing (NLP), and sentiment analysis. The proliferation of social media platforms has led to an explosion in the amount of user-generated content, creating both opportunities and challenges for understanding and predicting user behaviors. Recent advancements in machine learning and deep learning have paved the way for sophisticated techniques to extract and analyze this information, revealing valuable insights into user behavior. A major part of this review discusses how NLP techniques, combined with machine learning algorithms, have been effectively used for sentiment analysis to interpret and gauge user sentiments, fostering an understanding of trends, attitudes, and opinions in social networks. Moreover, we examine how these advanced methods have improved the accuracy of user behavior prediction, enabling more personalized and engaging experiences on these platforms. This comprehensive review not only provides a synthesized understanding of the current state-of-the-art methodologies but also identifies promising directions for future research in the mining of user behavior patterns in online social networks.
Related Topics
Concepts
Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.17762/msea.v70i2.2472
- https://philstat.org/index.php/MSEA/article/download/2472/1943
- OA Status
- bronze
- Cited By
- 2
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4380482032
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4380482032Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.17762/msea.v70i2.2472Digital Object Identifier
- Title
-
Mining User Behavior Patterns in Online Social Networks: A Review of Recent AdvancesWork title
- Type
-
reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-02-26Full publication date if available
- Authors
-
Poonam VermaList of authors in order
- Landing page
-
https://doi.org/10.17762/msea.v70i2.2472Publisher landing page
- PDF URL
-
https://philstat.org/index.php/MSEA/article/download/2472/1943Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://philstat.org/index.php/MSEA/article/download/2472/1943Direct OA link when available
- Concepts
-
Computer science, Sentiment analysis, Artificial intelligence, Social media, Data science, Deep learning, Machine learning, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
12Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4380482032 |
|---|---|
| doi | https://doi.org/10.17762/msea.v70i2.2472 |
| ids.doi | https://doi.org/10.17762/msea.v70i2.2472 |
| ids.openalex | https://openalex.org/W4380482032 |
| fwci | 0.28220715 |
| type | review |
| title | Mining User Behavior Patterns in Online Social Networks: A Review of Recent Advances |
| biblio.issue | 2 |
| biblio.volume | 70 |
| biblio.last_page | 1799 |
| biblio.first_page | 1793 |
| topics[0].id | https://openalex.org/T10664 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9988999962806702 |
| 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 | Sentiment Analysis and Opinion Mining |
| topics[1].id | https://openalex.org/T10064 |
| topics[1].field.id | https://openalex.org/fields/31 |
| topics[1].field.display_name | Physics and Astronomy |
| topics[1].score | 0.9987000226974487 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3109 |
| topics[1].subfield.display_name | Statistical and Nonlinear Physics |
| topics[1].display_name | Complex Network Analysis Techniques |
| topics[2].id | https://openalex.org/T11644 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9923999905586243 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1710 |
| topics[2].subfield.display_name | Information Systems |
| topics[2].display_name | Spam and Phishing Detection |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7152045369148254 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C66402592 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6600861549377441 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2271421 |
| concepts[1].display_name | Sentiment analysis |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.622286319732666 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C518677369 |
| concepts[3].level | 2 |
| concepts[3].score | 0.585737407207489 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q202833 |
| concepts[3].display_name | Social media |
| concepts[4].id | https://openalex.org/C2522767166 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5030190348625183 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[4].display_name | Data science |
| concepts[5].id | https://openalex.org/C108583219 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4878499507904053 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[5].display_name | Deep learning |
| concepts[6].id | https://openalex.org/C119857082 |
| concepts[6].level | 1 |
| concepts[6].score | 0.48485267162323 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[6].display_name | Machine learning |
| concepts[7].id | https://openalex.org/C136764020 |
| concepts[7].level | 1 |
| concepts[7].score | 0.3304874300956726 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q466 |
| concepts[7].display_name | World Wide Web |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7152045369148254 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/sentiment-analysis |
| keywords[1].score | 0.6600861549377441 |
| keywords[1].display_name | Sentiment analysis |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.622286319732666 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/social-media |
| keywords[3].score | 0.585737407207489 |
| keywords[3].display_name | Social media |
| keywords[4].id | https://openalex.org/keywords/data-science |
| keywords[4].score | 0.5030190348625183 |
| keywords[4].display_name | Data science |
| keywords[5].id | https://openalex.org/keywords/deep-learning |
| keywords[5].score | 0.4878499507904053 |
| keywords[5].display_name | Deep learning |
| keywords[6].id | https://openalex.org/keywords/machine-learning |
| keywords[6].score | 0.48485267162323 |
| keywords[6].display_name | Machine learning |
| keywords[7].id | https://openalex.org/keywords/world-wide-web |
| keywords[7].score | 0.3304874300956726 |
| keywords[7].display_name | World Wide Web |
| language | en |
| locations[0].id | doi:10.17762/msea.v70i2.2472 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210188813 |
| locations[0].source.issn | 2094-0343 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 2094-0343 |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Mathematical Statistician and Engineering Applications |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | |
| locations[0].pdf_url | https://philstat.org/index.php/MSEA/article/download/2472/1943 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Mathematical Statistician and Engineering Applications |
| locations[0].landing_page_url | https://doi.org/10.17762/msea.v70i2.2472 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5101537058 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-3302-6907 |
| authorships[0].author.display_name | Poonam Verma |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I60054993 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Computing, Graphic Era Hill University, Dehradun, Uttarakhand India 248002 |
| authorships[0].institutions[0].id | https://openalex.org/I60054993 |
| authorships[0].institutions[0].ror | https://ror.org/03wqgqd89 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I60054993 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Graphic Era University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Poonam Verma |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | School of Computing, Graphic Era Hill University, Dehradun, Uttarakhand India 248002 |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://philstat.org/index.php/MSEA/article/download/2472/1943 |
| open_access.oa_status | bronze |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Mining User Behavior Patterns in Online Social Networks: A Review of Recent Advances |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10664 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9988999962806702 |
| 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 | Sentiment Analysis and Opinion Mining |
| related_works | https://openalex.org/W2748952813, https://openalex.org/W2548633793, https://openalex.org/W2941935829, https://openalex.org/W3089396779, https://openalex.org/W2596247554, https://openalex.org/W4301373556, https://openalex.org/W3013279174, https://openalex.org/W3132372214, https://openalex.org/W4380075502, https://openalex.org/W2243502667 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2023 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.17762/msea.v70i2.2472 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210188813 |
| best_oa_location.source.issn | 2094-0343 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 2094-0343 |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Mathematical Statistician and Engineering Applications |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://philstat.org/index.php/MSEA/article/download/2472/1943 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Mathematical Statistician and Engineering Applications |
| best_oa_location.landing_page_url | https://doi.org/10.17762/msea.v70i2.2472 |
| primary_location.id | doi:10.17762/msea.v70i2.2472 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210188813 |
| primary_location.source.issn | 2094-0343 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 2094-0343 |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Mathematical Statistician and Engineering Applications |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | |
| primary_location.pdf_url | https://philstat.org/index.php/MSEA/article/download/2472/1943 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Mathematical Statistician and Engineering Applications |
| primary_location.landing_page_url | https://doi.org/10.17762/msea.v70i2.2472 |
| publication_date | 2021-02-26 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W6821113053, https://openalex.org/W6760385162, https://openalex.org/W6666761814, https://openalex.org/W40549020, https://openalex.org/W2251939518, https://openalex.org/W2153579005, https://openalex.org/W6691431627, https://openalex.org/W2969560515, https://openalex.org/W2896457183, https://openalex.org/W2250539671, https://openalex.org/W4294170691, https://openalex.org/W4239510810 |
| referenced_works_count | 12 |
| abstract_inverted_index.A | 86 |
| abstract_inverted_index.a | 155 |
| abstract_inverted_index.an | 40, 115 |
| abstract_inverted_index.in | 6, 11, 42, 61, 122, 171, 178 |
| abstract_inverted_index.of | 19, 33, 45, 89, 117, 136, 158, 174 |
| abstract_inverted_index.on | 16, 146 |
| abstract_inverted_index.to | 39, 74, 108 |
| abstract_inverted_index.we | 126 |
| abstract_inverted_index.NLP | 94 |
| abstract_inverted_index.The | 31 |
| abstract_inverted_index.and | 28, 51, 55, 64, 76, 110, 120, 143 |
| abstract_inverted_index.but | 163 |
| abstract_inverted_index.for | 53, 71, 105, 168 |
| abstract_inverted_index.has | 37 |
| abstract_inverted_index.how | 93, 128 |
| abstract_inverted_index.led | 38 |
| abstract_inverted_index.not | 152 |
| abstract_inverted_index.the | 3, 17, 43, 69, 134, 159, 172 |
| abstract_inverted_index.way | 70 |
| abstract_inverted_index.This | 0, 149 |
| abstract_inverted_index.also | 164 |
| abstract_inverted_index.been | 102 |
| abstract_inverted_index.both | 49 |
| abstract_inverted_index.deep | 22, 65 |
| abstract_inverted_index.have | 67, 101, 132 |
| abstract_inverted_index.into | 83 |
| abstract_inverted_index.more | 141 |
| abstract_inverted_index.only | 153 |
| abstract_inverted_index.part | 88 |
| abstract_inverted_index.this | 78, 90 |
| abstract_inverted_index.used | 104 |
| abstract_inverted_index.user | 8, 57, 84, 112, 137, 175 |
| abstract_inverted_index.with | 97 |
| abstract_inverted_index.gauge | 111 |
| abstract_inverted_index.major | 87 |
| abstract_inverted_index.media | 35 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.paved | 68 |
| abstract_inverted_index.these | 129, 147 |
| abstract_inverted_index.(NLP), | 27 |
| abstract_inverted_index.Recent | 59 |
| abstract_inverted_index.amount | 44 |
| abstract_inverted_index.future | 169 |
| abstract_inverted_index.mining | 7, 173 |
| abstract_inverted_index.online | 12, 179 |
| abstract_inverted_index.recent | 4 |
| abstract_inverted_index.review | 91, 151 |
| abstract_inverted_index.social | 13, 34, 123, 180 |
| abstract_inverted_index.Natural | 24 |
| abstract_inverted_index.analyze | 77 |
| abstract_inverted_index.current | 160 |
| abstract_inverted_index.examine | 127 |
| abstract_inverted_index.extract | 75 |
| abstract_inverted_index.machine | 20, 62, 98 |
| abstract_inverted_index.methods | 131 |
| abstract_inverted_index.reviews | 2 |
| abstract_inverted_index.trends, | 118 |
| abstract_inverted_index.Language | 25 |
| abstract_inverted_index.accuracy | 135 |
| abstract_inverted_index.advanced | 130 |
| abstract_inverted_index.advances | 5 |
| abstract_inverted_index.analysis | 107 |
| abstract_inverted_index.behavior | 9, 138, 176 |
| abstract_inverted_index.combined | 96 |
| abstract_inverted_index.content, | 47 |
| abstract_inverted_index.creating | 48 |
| abstract_inverted_index.enabling | 140 |
| abstract_inverted_index.engaging | 144 |
| abstract_inverted_index.focusing | 15 |
| abstract_inverted_index.improved | 133 |
| abstract_inverted_index.insights | 82 |
| abstract_inverted_index.learning | 63, 66, 99 |
| abstract_inverted_index.opinions | 121 |
| abstract_inverted_index.patterns | 10, 177 |
| abstract_inverted_index.provides | 154 |
| abstract_inverted_index.research | 170 |
| abstract_inverted_index.valuable | 81 |
| abstract_inverted_index.Moreover, | 125 |
| abstract_inverted_index.analysis. | 30 |
| abstract_inverted_index.behavior. | 85 |
| abstract_inverted_index.discusses | 92 |
| abstract_inverted_index.explosion | 41 |
| abstract_inverted_index.fostering | 114 |
| abstract_inverted_index.interpret | 109 |
| abstract_inverted_index.learning, | 21, 23 |
| abstract_inverted_index.networks, | 14 |
| abstract_inverted_index.networks. | 124, 181 |
| abstract_inverted_index.platforms | 36 |
| abstract_inverted_index.promising | 166 |
| abstract_inverted_index.revealing | 80 |
| abstract_inverted_index.sentiment | 29, 106 |
| abstract_inverted_index.Processing | 26 |
| abstract_inverted_index.attitudes, | 119 |
| abstract_inverted_index.behaviors. | 58 |
| abstract_inverted_index.challenges | 52 |
| abstract_inverted_index.directions | 167 |
| abstract_inverted_index.identifies | 165 |
| abstract_inverted_index.platforms. | 148 |
| abstract_inverted_index.predicting | 56 |
| abstract_inverted_index.techniques | 73 |
| abstract_inverted_index.algorithms, | 100 |
| abstract_inverted_index.effectively | 103 |
| abstract_inverted_index.experiences | 145 |
| abstract_inverted_index.integration | 18 |
| abstract_inverted_index.prediction, | 139 |
| abstract_inverted_index.sentiments, | 113 |
| abstract_inverted_index.synthesized | 156 |
| abstract_inverted_index.techniques, | 95 |
| abstract_inverted_index.advancements | 60 |
| abstract_inverted_index.information, | 79 |
| abstract_inverted_index.personalized | 142 |
| abstract_inverted_index.comprehensive | 150 |
| abstract_inverted_index.methodologies | 162 |
| abstract_inverted_index.opportunities | 50 |
| abstract_inverted_index.proliferation | 32 |
| abstract_inverted_index.sophisticated | 72 |
| abstract_inverted_index.understanding | 54, 116, 157 |
| abstract_inverted_index.user-generated | 46 |
| abstract_inverted_index.state-of-the-art | 161 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5101537058 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I60054993 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7200000286102295 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.66955979 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |