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Redefining Event Detection and Information Dissemination: Lessons from X (Twitter) Data Streams and Beyond.

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Titel: Redefining Event Detection and Information Dissemination: Lessons from X (Twitter) Data Streams and Beyond.
Autoren: Srivastava, Harshit, Sankar, Ravi
Quelle: Computers (2073-431X); Feb2025, Vol. 14 Issue 2, p42, 19p
Schlagwörter: DETECTION algorithms, SOCIAL computing, NATURAL language processing, DATA analytics, INFORMATION dissemination
Abstract: X (formerly known as Twitter), Reddit, and other social media forums have dramatically changed the way society interacts with live events in this day and age. The huge amount of data generated by these platforms presents challenges, especially in terms of processing speed and the complexity of finding meaningful patterns and events. These data streams are generated in multiple formats, with constant updating, and are real-time in nature; thus, they require sophisticated algorithms capable of dynamic event detection in this dynamic environment. Event detection techniques have recently achieved substantial development, but most research carried out so far evaluates only single methods, not comparing the overall performance of these methods across multiple platforms and types of data. With that view, this paper represents a deep investigation of complex state-of-the-art event detection algorithms specifically customized for streams of data from X. We review various current techniques based on a thorough comparative performance test and point to problems inherently related to the detection of patterns in high-velocity streams with noise. We introduce some novelty to this research area, supported by appropriate robust experimental frameworks, to performed comparisons quantitatively and qualitatively. We provide insight into how those algorithms perform under varying conditions by defining a set of clear, measurable metrics. Our findings contribute new knowledge that will help inform future research into the improvement of event detection systems for dynamic data streams and enhance their capabilities for real-time and actionable insights. This paper will go a step further than the present knowledge of event detection and discuss how algorithms can be adapted and refined in view of the emerging demands imposed by data streams. [ABSTRACT FROM AUTHOR]
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  Data: Redefining Event Detection and Information Dissemination: Lessons from X (Twitter) Data Streams and Beyond.
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  Data: <searchLink fieldCode="AR" term="%22Srivastava%2C+Harshit%22">Srivastava, Harshit</searchLink><br /><searchLink fieldCode="AR" term="%22Sankar%2C+Ravi%22">Sankar, Ravi</searchLink>
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  Data: Computers (2073-431X); Feb2025, Vol. 14 Issue 2, p42, 19p
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  Data: X (formerly known as Twitter), Reddit, and other social media forums have dramatically changed the way society interacts with live events in this day and age. The huge amount of data generated by these platforms presents challenges, especially in terms of processing speed and the complexity of finding meaningful patterns and events. These data streams are generated in multiple formats, with constant updating, and are real-time in nature; thus, they require sophisticated algorithms capable of dynamic event detection in this dynamic environment. Event detection techniques have recently achieved substantial development, but most research carried out so far evaluates only single methods, not comparing the overall performance of these methods across multiple platforms and types of data. With that view, this paper represents a deep investigation of complex state-of-the-art event detection algorithms specifically customized for streams of data from X. We review various current techniques based on a thorough comparative performance test and point to problems inherently related to the detection of patterns in high-velocity streams with noise. We introduce some novelty to this research area, supported by appropriate robust experimental frameworks, to performed comparisons quantitatively and qualitatively. We provide insight into how those algorithms perform under varying conditions by defining a set of clear, measurable metrics. Our findings contribute new knowledge that will help inform future research into the improvement of event detection systems for dynamic data streams and enhance their capabilities for real-time and actionable insights. This paper will go a step further than the present knowledge of event detection and discuss how algorithms can be adapted and refined in view of the emerging demands imposed by data streams. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Computers (2073-431X) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.3390/computers14020042
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      – Code: eng
        Text: English
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        PageCount: 19
        StartPage: 42
    Subjects:
      – SubjectFull: DETECTION algorithms
        Type: general
      – SubjectFull: SOCIAL computing
        Type: general
      – SubjectFull: NATURAL language processing
        Type: general
      – SubjectFull: DATA analytics
        Type: general
      – SubjectFull: INFORMATION dissemination
        Type: general
    Titles:
      – TitleFull: Redefining Event Detection and Information Dissemination: Lessons from X (Twitter) Data Streams and Beyond.
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            NameFull: Srivastava, Harshit
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              M: 02
              Text: Feb2025
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              Y: 2025
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