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Identification of Psychological Harassment Via Digital Communication Media

Manorama Singh1 , Abhay Kumar2

  1. Dept. of CSE, J.B. Institute of Engineering and Technology, Hyderabad, India.
  2. Dept. of CSE, J.B. Institute of Engineering and Technology, Hyderabad, India.

Section:Review Paper, Product Type: Journal Paper
Volume-5 , Issue-12 , Page no. 147-150, Dec-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i12.147150

Online published on Dec 31, 2017

Copyright © Manorama Singh, Abhay Kumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Manorama Singh, Abhay Kumar, “Identification of Psychological Harassment Via Digital Communication Media,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.147-150, 2017.

MLA Style Citation: Manorama Singh, Abhay Kumar "Identification of Psychological Harassment Via Digital Communication Media." International Journal of Computer Sciences and Engineering 5.12 (2017): 147-150.

APA Style Citation: Manorama Singh, Abhay Kumar, (2017). Identification of Psychological Harassment Via Digital Communication Media. International Journal of Computer Sciences and Engineering, 5(12), 147-150.

BibTex Style Citation:
@article{Singh_2017,
author = {Manorama Singh, Abhay Kumar},
title = {Identification of Psychological Harassment Via Digital Communication Media},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2017},
volume = {5},
Issue = {12},
month = {12},
year = {2017},
issn = {2347-2693},
pages = {147-150},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1594},
doi = {https://doi.org/10.26438/ijcse/v5i12.147150}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v5i12.147150}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1594
TI - Identification of Psychological Harassment Via Digital Communication Media
T2 - International Journal of Computer Sciences and Engineering
AU - Manorama Singh, Abhay Kumar
PY - 2017
DA - 2017/12/31
PB - IJCSE, Indore, INDIA
SP - 147-150
IS - 12
VL - 5
SN - 2347-2693
ER -

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Abstract

Although the Internet has transformed the way our world operates, it has also served as a venue for cyberbullying, a serious form of misbehavior among youth. With many of today`s youth experiencing acts of cyberbullying [2], a growing body of literature has begun to document the prevalence, predictors, and outcomes of this behavior, but the literature is highly fragmented and lacks theoretical focus. Therefore, our purpose in the present article [1] is to provide a critical review of the existing cyberbullying research. This systematic review and meta-analysis [6][7] offers a synthesis of the relationship between cyber-victimization and educational outcomes of adolescents aged 12 to 17, including 25 effect sizes from 12 studies drawn from a variety of disciplines. The general aggression model is proposed as a useful theoretical framework from which to understand this phenomenon. Additionally, results from a meta-analytic review are presented to highlight the size of the relationships between cyberbullying and traditional bullying, as well as relationships between cyberbullying and other meaningful behavioral and psychological variables. A series of random-effects meta-analyses [12] using robust variance estimation revealed associations between cyber-victimization [4] and higher class presence problems (r = .20) and academic achievement problems (r = .14). Results did not differ by provided definition, publication status, reporting time frame, gender, race/ethnicity, or average age. Implications for future research are discussed within context of theoretical, critical, and applied discussions.

Key-Words / Index Term

cyber-victimization, victimization, meta-analysis, adolescents, academic achievement, school attendance, Cyberbullying Detection, Text Mining, Representation Learning

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