See the published version of this preprint at Computational Social Networks.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Research
Meng Han
Kennesaw State University
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Social media provides high-volume and real-time data, which has been broadly used in diverse applications in sales, marketing, disaster management, health surveillance, etc. However, distinguishing between noises and reliable information can be challenging, since social media, a user-generated content system, has a great number of users who update massive information every second. The rich information is not only included in the short textual content but also embedded in the images and videos. In this paper, we introduce an effective and efficient framework for event detection with social media data. The framework integrates both textual and imagery content in the hope to fully utilize the information. The approach has been demonstrated to be more accurate than the text-only approach by removing 58 (66.7%) false-positive events. The precision of event detection is improved by 6.5%. Besides, based on our analysis, we also look into the content of these images to further explore the space of social media studies. Finally, the closely related text and image from social media offer us valuable text-image mapping, which can enable knowledge transfer between two media types.
Keywords
social media, multimedia information, event detection
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CURRENT STATUS: Published
View the published article at Computational Social Networks.
Version 2
Posted 16 Nov, 2020
No community comments so far
Editorial decision: Accept
On 17 Jan, 2021
Review #2 received
Received 22 Dec, 2020
Review #1 received
Received 22 Dec, 2020
Reviewer #2 agreed
On 17 Nov, 2020
Reviewer #1 agreed
On 14 Nov, 2020
Reviewers invited
Invitations sent on 13 Nov, 2020
Editor assigned
On 09 Nov, 2020
Submission checks complete
On 09 Nov, 2020
Editor invited
On 09 Nov, 2020
No comments provided
Editorial decision: Major Revision
On 12 Jul, 2020
Review #2 received
Received 11 Jul, 2020
Review #1 received
Received 03 Jul, 2020
Review #3 received
Received 06 May, 2020
Reviewer #3 agreed
On 06 May, 2020
Reviewer #2 agreed
On 05 May, 2020
Reviewer #1 agreed
On 04 May, 2020
Reviewers invited
Invitations sent on 01 May, 2020
Editor assigned
On 23 Apr, 2020
First submitted
On 22 Apr, 2020
Submission checks complete
On 22 Apr, 2020
Editor invited
On 22 Apr, 2020
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See the published version of this preprint at Computational Social Networks.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Meng Han
Kennesaw State University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Computational Social Networks.
Version 2
Posted 16 Nov, 2020
No community comments so far
Editorial decision: Accept
On 17 Jan, 2021
Review #2 received
Received 22 Dec, 2020
Review #1 received
Received 22 Dec, 2020
Reviewer #2 agreed
On 17 Nov, 2020
Reviewer #1 agreed
On 14 Nov, 2020
Reviewers invited
Invitations sent on 13 Nov, 2020
Editor assigned
On 09 Nov, 2020
Submission checks complete
On 09 Nov, 2020
Editor invited
On 09 Nov, 2020
No comments provided
Editorial decision: Major Revision
On 12 Jul, 2020
Review #2 received
Received 11 Jul, 2020
Review #1 received
Received 03 Jul, 2020
Review #3 received
Received 06 May, 2020
Reviewer #3 agreed
On 06 May, 2020
Reviewer #2 agreed
On 05 May, 2020
Reviewer #1 agreed
On 04 May, 2020
Reviewers invited
Invitations sent on 01 May, 2020
Editor assigned
On 23 Apr, 2020
First submitted
On 22 Apr, 2020
Submission checks complete
On 22 Apr, 2020
Editor invited
On 22 Apr, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Computational Social Networks.
Social media provides high-volume and real-time data, which has been broadly used in diverse applications in sales, marketing, disaster management, health surveillance, etc. However, distinguishing between noises and reliable information can be challenging, since social media, a user-generated content system, has a great number of users who update massive information every second. The rich information is not only included in the short textual content but also embedded in the images and videos. In this paper, we introduce an effective and efficient framework for event detection with social media data. The framework integrates both textual and imagery content in the hope to fully utilize the information. The approach has been demonstrated to be more accurate than the text-only approach by removing 58 (66.7%) false-positive events. The precision of event detection is improved by 6.5%. Besides, based on our analysis, we also look into the content of these images to further explore the space of social media studies. Finally, the closely related text and image from social media offer us valuable text-image mapping, which can enable knowledge transfer between two media types.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
This preprint is available for download as a PDF.
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