Showing 1–50 of 57 papers, ordered by publication year (newest first), then title.
Age markers as of 2026-11-06.
Stéphane Gaël R. Ekodeck; Serge Alain Ebele; Vivien Beyala Kamgang; Eugène Géraldin Awono Atangana; Stéphane Willy Mossebo Tcheunteu; Damaris Belle M. Fotso; Georges Bell Bitjoka; Julius Marcellin Nkenlifack (2026) · IEEE DataPort
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Dika Sri Pandanari (2026) · Zenodo (CERN European Organization for Nuclear Research)
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Vinden John Wylde; Abbas Fadhil Aljuboori (2026) · European Conference on Social Media
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Nonye Peter Awurum (2026) · Zenodo (CERN European Organization for Nuclear Research)
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Fabio Giglietto; Cornelius Puschmann (2026) · M/C Journal
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Klaus Saalbach (2026) · osnaDocs (Osnabrück University)
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Kayla Barral; Carter Pedersen; Elise Stevens (2026) · UND Scholarly Commons (University of North Dakota)
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Mackenzie Deborah Kaddu; Sam Njumwamukama (2026) · International Journal of Scientific Research and Modern Technology
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BECHIR MAHAMAT AHMAT MARKHOUS; Hassan Abdoulaye Abakar (2026) · Zenodo (CERN European Organization for Nuclear Research)
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Alfred Hermida; Axel Bruns (2026) · M/C Journal
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Leysia Palen (2026) · M/C Journal
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Kateryna Bystrytska (2026) · Digital War
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Raghavendra Cherupalli; Hawken Grubbs; Yi Ting Chua; Weiping Pei; Tyler Moore; Gary Warner (2025) · 2025 APWG Symposium on Electronic Crime Research (eCrime)
Collection: APWG.org eCrimes Research · Original collection
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Roy Ricaldi; Tina Marjanov; Luca Allodi; Alice Hutchings (2025) · 2025 APWG Symposium on Electronic Crime Research (eCrime)
Collection: APWG.org eCrimes Research · Original collection
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Sylvia Worlali Azumah; Victor Adewopo; Zag Elsayed; Nelly Elsayed; Murat Ozer (2024) · IEEE International Conference on AI in Cybersecurity (ICAIC)
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Analyzing Social Media Activities at Bellingcat Bär et al. (2023)
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Author metadata incomplete (2021)
Paper details Research context This work reflects the evidence and technical environment at the time of publication. Its methods and insights may remain valuable, while some findings or practical guidance may have been refined or superseded by subsequent research and developments. Please consult more recent sources before applying it to current research or investigations.
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Author metadata incomplete (2021) · Springer
Paper details Research context This work reflects the evidence and technical environment at the time of publication. Its methods and insights may remain valuable, while some findings or practical guidance may have been refined or superseded by subsequent research and developments. Please consult more recent sources before applying it to current research or investigations.
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Author metadata incomplete (2021) · Elsevier / ScienceDirect
Paper details Research context This work reflects the evidence and technical environment at the time of publication. Its methods and insights may remain valuable, while some findings or practical guidance may have been refined or superseded by subsequent research and developments. Please consult more recent sources before applying it to current research or investigations.
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Author metadata incomplete (2021) · Springer
Paper details Research context This work reflects the evidence and technical environment at the time of publication. Its methods and insights may remain valuable, while some findings or practical guidance may have been refined or superseded by subsequent research and developments. Please consult more recent sources before applying it to current research or investigations.
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Author metadata incomplete (2021) · ACM Digital Library
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Author metadata incomplete (2020) · MDPI
Paper details Research context This work reflects the evidence and technical environment at the time of publication. Its methods and insights may remain valuable, while some findings or practical guidance may have been refined or superseded by subsequent research and developments. Please consult more recent sources before applying it to current research or investigations.
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Author metadata incomplete (2020)
Paper details Research context This work reflects the evidence and technical environment at the time of publication. Its methods and insights may remain valuable, while some findings or practical guidance may have been refined or superseded by subsequent research and developments. Please consult more recent sources before applying it to current research or investigations.
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Author metadata incomplete (2020) · IEEE
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Author metadata incomplete (2020) · IEEE
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Author metadata incomplete (2019) · IEEE
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Author metadata incomplete (2019) · Springer
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Author metadata incomplete (2019) · Elsevier / ScienceDirect
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Author metadata incomplete (2019) · Elsevier / ScienceDirect
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Author metadata incomplete (2019)
Paper details Research context This work reflects the evidence and technical environment at the time of publication. Its methods and insights may remain valuable, while some findings or practical guidance may have been refined or superseded by subsequent research and developments. Please consult more recent sources before applying it to current research or investigations.
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Author metadata incomplete (2019)
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Twitter Sentiment Analysis using Distributed Word and Sentence Representation 5+ Author metadata incomplete (2019)
Paper details Research context This work reflects the evidence and technical environment at the time of publication. Its methods and insights may remain valuable, while some findings or practical guidance may have been refined or superseded by subsequent research and developments. Please consult more recent sources before applying it to current research or investigations.
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Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information 5+ Author metadata incomplete (2019)
Paper details Research context This work reflects the evidence and technical environment at the time of publication. Its methods and insights may remain valuable, while some findings or practical guidance may have been refined or superseded by subsequent research and developments. Please consult more recent sources before applying it to current research or investigations.
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Author metadata incomplete (2018) · IEEE
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Dynamic Large Scale Data on Twitter using Sentiment Analysis and Topic Modeling 5+ Author metadata incomplete (2018)
Paper details Research context This work reflects the evidence and technical environment at the time of publication. Its methods and insights may remain valuable, while some findings or practical guidance may have been refined or superseded by subsequent research and developments. Please consult more recent sources before applying it to current research or investigations.
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Author metadata incomplete (2018) · MDPI
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Mining Location from Social Media: A Systematic Review 5+ Stock (2018)
Paper details Research context This work reflects the evidence and technical environment at the time of publication. Its methods and insights may remain valuable, while some findings or practical guidance may have been refined or superseded by subsequent research and developments. Please consult more recent sources before applying it to current research or investigations.
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Author metadata incomplete (2018) · ACM Digital Library
Paper details Research context This work reflects the evidence and technical environment at the time of publication. Its methods and insights may remain valuable, while some findings or practical guidance may have been refined or superseded by subsequent research and developments. Please consult more recent sources before applying it to current research or investigations.
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Author metadata incomplete (2017) · Springer
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Author metadata incomplete (2017)
Paper details Research context This work reflects the evidence and technical environment at the time of publication. Its methods and insights may remain valuable, while some findings or practical guidance may have been refined or superseded by subsequent research and developments. Please consult more recent sources before applying it to current research or investigations.
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Social Media Sentiment Analysis using Machine Learning Classifiers 5+ Author metadata incomplete (2017)
Paper details Research context This work reflects the evidence and technical environment at the time of publication. Its methods and insights may remain valuable, while some findings or practical guidance may have been refined or superseded by subsequent research and developments. Please consult more recent sources before applying it to current research or investigations.
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Author metadata incomplete (2016)
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Author metadata incomplete (2016) · Elsevier / ScienceDirect
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Identifying Informative Messages in Disaster Events using Convolutional Neural Networks 5+ Author metadata incomplete (2016)
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Opinion mining and sentiment analysis 5+ Author metadata incomplete (2016)
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Author metadata incomplete (2016) · Springer
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Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality 5+ Author metadata incomplete (2016)
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Author metadata incomplete (2015) · ACM Digital Library
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A Review of Techniques for Sentiment Analysis Of Twitter Data 5+ Author metadata incomplete (2014)
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Author metadata incomplete (2014) · IEEE
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