Intelligent Approach for Identifying Political Views over Social Networks

Yavanoğlu, Uraz; Çolak, Medine; Caglar, Busra; Cakir, Semra; Milletsever, Ozlem; Sağıroğlu, Şeref

doi:10.1109/icmla.2013.136

Cited by 4 publications

(1 citation statement)

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“…In these events, the organization of protests was achieved through Twitter and the governments put a ban on Twitter. It is claimed that a system developed to classify user accounts as either being a supporter or non-supporter, achieved 90% accuracy while analysing Twitter accounts related to the Gezi protests (Yavanoglu, et al, 2013).…”

Section: Analysis Of the Data Subject To The Crime On The Internet And Social Mediamentioning

confidence: 99%

Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

Güzel¹

2021

JPLC

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Young adults engage in a disproportionately high rate of problematic behaviors such as risky sexual activities, academic dishonesty, and substance abuse. In order to understand why this occurs, two lesser-known constructs related to risktaking behavior were investigated in this study: differential identification, which has yet to be empirically studied in the context of emerging adults, and criminogenic thinking, which has only been evaluated in this context to a minimal degree. To bridge the gap between these two constructs, motivation was hypothesized to be a moderator in each of these respective relationships. Data from 309 emerging adults were recruited via Amazon's Mechanical Turk (MTurk) to complete the survey materials online. The analysis consisted of a hierarchical multiple linear regression. Results indicated that motivation did not moderate either relationship (i.e., differential identification and risk-taking behavior and criminogenic thinking and risk-taking behavior). However, a main effect was found between one aspect of differential identification and risk-taking behavior. No other main effects were found in this study. Potential explanations for the results of this study are discussed, as well as implications and directions for future research.

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Section: Analysis Of the Data Subject To The Crime On The Internet And Social Mediamentioning

confidence: 99%