Bullying Behavior and Project-based Activities in Virtual Learning Communities (VLCs)

The Modern Greek Language on the Social Web: A Survey of Data Sets and Mining Applications

“…The identification of patterns in data has been a demanding task in the context of social text, mainly due to its unstructured nature, rich morphology and increasing volume [2,4,7,10,11,[13][14][15][16]. Several researchers have focused on the identification of linguistic and/or behavioral patterns of interest in social text data.…”

“…Sardianos et al [15] attempted the identification of Modern Greek social text segments that contained arguments, by creating a novel, manually annotated corpus 16 and applying semi-supervised learning with CRF. The proposed approach focused mostly on e-Government and the policy making domain.…”

“…There are several approaches that have attempted to detect and analyze bullying and aggressive behavior in Virtual Learning Communities (VLCs) [4,16,17]. Other work focuses on offensive language identification and analysis in tweets [6,18].…”

“…The effect of project-based activities on the aggressive behavior of the learners in VLCs was explored by the same authors in Nikiforos et al [16]. Two PLCs (different from those of [4]) were transformed into VLCs in the Wikispaces web-based collaborative platform.…”

“…The increasing number of users connecting through social networks and web platforms, such as Facebook and Twitter, as well as numerous Blogs and Wikis, creates continuously a significant volume in written communication through the Web [1][2][3][4][5][6][7]. The amount and quality of information and knowledge extracted from social text has been considered crucial to studying and analyzing public opinion [1,3,5,8,9], as well as linguistic [2,7,[10][11][12][13][14][15] and behavioral [4,6,[16][17][18] patterns. In its typical form, social text is often short in length, low in readability scores, informal, syntactically unstructured, characterized by great morphological diversity and features of oral speech, misspellings and slang vocabulary, consequently presenting major challenges for NLP and Data Mining tasks [2,4,7,10,11,13,[13][14][15][16]19].…”