Evaluating Web Search Engines Results for Personalization and User Tracking

Recently, light has been shed on the trend of personalization, which comes into play whenever different search results are being tailored for a group of users who have issued the same search query. The unpalatable fact that myriads of search results are being manipulated has perturbed a horde of people. With regards to that, personalization can be instrumental in spurring the Filter Bubble effects, which revolves around the inability of certain users to gain access to the typified contents that are allegedly irrelevant per the search engine's algorithm. In harmony with that, there is a wealth of research on this area. Each of these has relied on using techniques revolving around creating Google accounts that differ in one feature and issuing identical search queries from each account. The search results are often compared to determine whether those results are going to vary per account. Thereupon, we have conducted six experiments that aim to closely inspect and spot the patterns of personalization in search results. In a like manner, we are going to examine how the search results are going to vary accordingly. In all of the tasks, three different metrics are going to be measured, namely, the number of total hits, the first hit, and the correlation between hits. Those experiments are centered around fulfilling the following tasks. Firstly, setting up four VPNs that are located at different geographic locations and comparing the search results with those obtained in the UAE. Secondly, performing the search while logging in and out of a Google account. Thirdly, searching while connecting to different networks: home, phone, and university networks. Fourthly, using different search engines to issue the search queries. Fifthly, using different web browsers to carry out the search process. Finally, creating and training six Google accounts.

FALSE: Fake News Automatic and Lightweight Solution

Fake news existed ever since there was news, from rumors to printed media then radio and television. Recently, the information age, with its communications and Internet breakthroughs, exacerbated the spread of fake news. Additionally, aside from e-Commerce, the current Internet economy is dependent on advertisements, views and clicks, which prompted many developers to bait the end users to click links or ads. Consequently, the wild spread of fake news through social media networks has impacted real world issues from elections to 5G adoption and the handling of the Covid- 19 pandemic. Efforts to detect and thwart fake news has been there since the advent of fake news, from fact checkers to artificial intelligence-based detectors. Solutions are still evolving as more sophisticated techniques are employed by fake news propagators. In this paper, R code have been used to study and visualize a modern fake news dataset. We use clustering, classification, correlation and various plots to analyze and present the data. The experiments show high efficiency of classifiers in telling apart real from fake news.