scholarly journals The Construction of the Tales Teller. A study on Several Interviews with Nicolas Bouvier

Viatica ◽  
2017 ◽  
Author(s):  
Philippe ANTOINE ◽  
Keyword(s):  

This article focuses on the figure of Nicolas Bouvier as a tales teller. The recurring presence of the storytelling figure in Bouvier’s work demonstrates an obvious interest in orality, visible particularly in discussions, cinematographic productions or interviews. This study, which focuses on three of Bouvier’s public performances (Routes et Déroutes, a film portrayal entitled Le Hibou et la Baleine and a series of documents from Radio Télévision Suisse romande) examines the writer-traveller’s position as well the different textual, image and sound representations of his work.

1994 ◽  
Vol 82 (6) ◽  
pp. 878-888 ◽  
Author(s):  
I.H. Witten ◽  
T.C. Bell ◽  
H. Emberson ◽  
S. Inglis ◽  
A. Moffat

Author(s):  
I.H. Witten ◽  
T.C. Bell ◽  
M.-E. Harrison ◽  
M.L. James ◽  
A. Moffat

2015 ◽  
Vol 34 (2) ◽  
pp. 131-142 ◽  
Author(s):  
Hadar Averbuch-Elor ◽  
Yunhai Wang ◽  
Yiming Qian ◽  
Minglun Gong ◽  
Johannes Kopf ◽  
...  
Keyword(s):  

Author(s):  
Ye zhiwei ◽  
◽  
Yang Juan ◽  
Zhang Xu ◽  
Hu Zhengbing

Author(s):  
Arti Jain ◽  
Reetika Gairola ◽  
Shikha Jain ◽  
Anuja Arora

Spam on the online social networks (OSNs) is evolving as a prominent problem for the users of these networks. Spammers often use certain techniques to deceive the OSN users for their own benefit. Facebook, one of the leading OSNs, is experiencing such crucial problems at an alarming rate. This chapter presents a methodology to segregate spam from legitimate posts using machine learning techniques: naïve Bayes (NB), support vector machine (SVM), and random forest (RF). The textual, image, and video features are used together, which wasn't considered by the earlier researchers. Then, 1.5 million posts and comments are extracted from archival and real-time Facebook data, which is then pre-processed using RStudio. A total of 30 features are identified, out of which 10 are the best informative for identification of spam vs. ham posts. The entire dataset is shuffled and divided into three ratios, out of which 80:20 ratio of training and testing dataset provides the best result. Also, RF classifier outperforms NB and SVM by achieving overall F-measure 89.4% on the combined feature set.


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