Making Sense of Governmental Activities Over Social Media: A Data-Driven Approach

2015 ◽  
pp. 34-45 ◽  
Author(s):  
Brunella Caroleo ◽  
Andrea Tosatto ◽  
Michele Osella
Keyword(s):  
Social Media ◽  
Data Driven ◽  
Making Sense ◽  
Data Driven Approach

Effective Promotional Strategies Selection in Social Media: A Data-Driven Approach

2018 ◽  
Vol 4 (4) ◽  
pp. 487-501 ◽  
Author(s):  
Kun Kuang ◽  
Meng Jiang ◽  
Peng Cui ◽  
Hengliang Luo ◽  
Shiqiang Yang
Keyword(s):  
Social Media ◽  
Data Driven ◽  
Data Driven Approach ◽  
Promotional Strategies

Automatic Detection and Analysis of the “Game Hack” Scam

2020 ◽  
Author(s):  
Emad Badawi ◽  
Guy-Vincent Jourdan ◽  
Gregor Bochmann ◽  
Iosif-Viorel Onut
Keyword(s):  
Social Media ◽  
Market Research ◽  
Large Scale ◽  
Personal Information ◽  
Data Driven ◽  
Search Queries ◽  
Vast Array ◽  
Low Profile ◽  
Data Driven Approach ◽  
Insight Into

The “Game Hack” Scam (GHS) is a mostly unreported cyberattack in which attackers attempt to convince victims that they will be provided with free, unlimited “resources” or other advantages for their favorite game. The endgame of the scammers ranges from monetizing for themselves the victims time and resources by having them click through endless “surveys”, filing out “market research” forms, etc., to collecting personal information, getting the victims to subscribe to questionable services, up to installing questionable executable files on their machines. Other scams such as the “Technical Support Scam”, the “Survey Scam”, and the “Romance Scam” have been analyzed before but to the best of our knowledge, GHS has not been well studied so far and is indeed mostly unknown. In this paper, our aim is to investigate and gain more knowledge on this type of scam by following a data-driven approach; we formulate GHS-related search queries, and used multiple search engines to collect data about the websites to which GHS victims are directed when they search online for various game hacks and tricks. We analyze the collected data to provide new insight into GHS and research the extent of this scam. We show that despite its low profile, the click traffic generated by the scam is in the hundreds of millions. We also show that GHS attackers use social media, streaming sites, blogs, and even unrelated sites such as change.org or jeuxvideo.com to carry out their attacks and reach a large number of victims. Our data collection spans a year; in that time, we uncovered 65,905 different GHS URLs, mapped onto over 5,900 unique domains.We were able to link attacks to attackers and found that they routinely target a vast array of games. Furthermore, we find that GHS instances are on the rise, and so is the number of victims. Our low-end estimation is that these attacks have been clicked at least 150 million times in the last five years. Finally, in keeping with similar large-scale scam studies, we find that the current public blacklists are inadequate and suggest that our method is more effective at detecting these attacks.

scholarly journals Youtube Video Ranking: A NLP based System

2019 ◽  
Vol 8 (4) ◽  
pp. 1370-1375
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Information Source ◽  
Rating System ◽  
Data Driven ◽  
Search Query ◽  
Video Information ◽  
User Comments ◽  
Data Driven Approach ◽  
The Web

YouTube is an acclaimed video information source on the web among various social media sites, where users are sharing, commenting and liking/dis-liking the video along with the continuous uploading of videos in real-time. Generally, the quality, popularity and relevance of results obtained from searching a query are obtained based on a rating system. Now and then few irrelevant and substandard videos are ranked higher because of higher views and likes. To address this issue, we put forth a sentiment analysis approach on the user comments based on Natural Language Processing. The suggested analysis will be helpful in providing a desirable result to the search query. The effectuality of the system has been proved in this paper using a data driven approach in terms of accuracy.

Exploring the dominant features of social media for depression detection

2019 ◽  
Vol 46 (6) ◽  
pp. 739-759
Author(s):  
Jamil Hussain ◽  
Fahad Ahmed Satti ◽  
Muhammad Afzal ◽  
Wajahat Ali Khan ◽  
Hafiz Syed Muhammad Bilal ◽  
...  
Keyword(s):  
Social Media ◽  
Epidemiological Studies ◽  
Data Driven ◽  
Patient Portal ◽  
Data Set ◽  
Machine Learning Classification ◽  
Patient Behaviour ◽  
Depression Detection ◽  
Mental Diseases ◽  
Data Driven Approach

Recently, social media have been used by researchers to detect depressive symptoms in individuals using linguistic data from users’ posts. In this study, we propose a framework to identify social information as a significant predictor of depression. Using the proposed framework, we develop an application called the Socially Mediated Patient Portal (SMPP), which detects depression-related markers in Facebook users by applying a data-driven approach with machine learning classification techniques. We examined a data set of 4350 users who were evaluated for depression using the Center for Epidemiological Studies Depression (CES-D) scale. From this analysis, we identified a set of features that can distinguish between individuals with and without depression. Finally, we identified the dominant features that adequately assess individuals with and without depression on social media. The model trained on these features will be helpful to physicians in diagnosing mental diseases and psychiatrists in analysing patient behaviour.

scholarly journals ”Satire Junge SATIREEEE”: Nachrichtenparodien in Memes und deren Aushandlung im Kontext des Web 2.0.

2020 ◽  
Vol 103 (3) ◽  
pp. 129-157
Author(s):  
Fabia Hultin Morger
Keyword(s):  
Social Media ◽  
Web 2.0 ◽  
Empirical Data ◽  
Data Driven ◽  
The Internet ◽  
Data Set ◽  
The Public ◽  
Political Events ◽  
Data Driven Approach ◽  
Tv Shows

Satire has been present in various different media throughout the centuries. With the rise of television, satire has made its way onto TV screens via various outlets including news parodies. As these TV shows began using social media, new forms of satire have appeared, among them satirical Internet Memes commenting on political events. The objects of interest in this study are Memes published by two German news parodies Heute Show and Extra 3 on the platform Facebook that thematise the G-20 summit, which took place in Hamburg in 2017. My data set consists of 27 Memes from the platforms Facebook, Instagram and Twitter, as well as the public Facebook comments published alongside these Memes. Using an empirical, data-driven approach to my investigation, I broach questions regarding the way Memes make use of satire and how they interact with the Internet as a medium, and in particular, their affordances on the platform Facebook.

Sign in / Sign up

Export Citation Format

Share Document