arXiv subjects

Giannis Haralabopoulos

Publications and source records attributed to Giannis Haralabopoulos.

6 recordsLinked to original sources

arXiv2022

Privacy-Aware Crowd Labelling for Machine Learning Tasks

The extensive use of online social media has highlighted the importance of privacy in the digital space. As more scientists analyse the data created in these platforms, privacy concerns have extended to data usage within the academia. Although text analysis is a well documented topic in academic literature with a multitude of applications, ensuring privacy of user-generated content has been overlooked. Most sentiment analysis methods require emotion labels, which can be obtained through crowdsourcing, where non-expert individuals contribute to scientific tasks. The text itself has to be exposed to third parties in order to be labelled. In an effort to reduce the exposure of online users' information, we propose a privacy preserving text labelling method for varying applications, based in crowdsourcing. We transform text with different levels of privacy, and analyse the effectiveness of the transformation with regards to label correlation and consistency. Our results suggest that privacy can be implemented in labelling, retaining the annotational diversity and subjectivity of traditional labelling.

cs.HC↗

arXiv2018

Viral content propagation in Online Social Networks

Information flows are the result of a constant exchange in Online Social Networks (OSNs). OSN users create and share varying types of information in real-time throughout a day. Virality is introduced as a term to describe information that reaches a wide audience within a small time-frame. As a case, we measure propagation of information submitted in Reddit, identify different patterns and present a multi OSN diffusion analysis on Twitter, Facebook, and 2 hosting domains for images and multimedia, ImgUr and YouTube. Our results indicate that positive content is the most shared and presents the highest virality probability, and the overall virality probability of user created information is low. Finally, we underline the problems of limited access in OSN data. Keywords: Online Social Networks, Virality, Diffusion, Viral Content, Reddit, Twitter, Facebook, ImgUr, YouTube

cs.SI↗

arXiv2017

Crowdsourcing for Beyond Polarity Sentiment Analysis A Pure Emotion Lexicon

Sentiment analysis aims to uncover emotions conveyed through information. In its simplest form, it is performed on a polarity basis, where the goal is to classify information with positive or negative emotion. Recent research has explored more nuanced ways to capture emotions that go beyond polarity. For these methods to work, they require a critical resource: a lexicon that is appropriate for the task at hand, in terms of the range of emotions it captures diversity. In the past, sentiment analysis lexicons have been created by experts, such as linguists and behavioural scientists, with strict rules. Lexicon evaluation was also performed by experts or gold standards. In our paper, we propose a crowdsourcing method for lexicon acquisition, which is scalable, cost-effective, and doesn't require experts or gold standards. We also compare crowd and expert evaluations of the lexicon, to assess the overall lexicon quality, and the evaluation capabilities of the crowd.

cs.CL↗

arXiv2015

An Anonymous Social Network of Opinions

Research interest on Online Social Networks (OSNs), has increased dramatically over the last decade, mainly because online networks provide a vast source of social information. Graph structure, user connections, growth, information exposure and diffusion, are some of the most frequently researched subjects. However, some areas of these networks, such as anonymity, equality and bias are overlooked or even unconsidered. In the related bibliography, such features seem to be influential to social interactions. Based on these studies, we aim at determining how universal anonymity affects bias, user equality, information propagation, sharing and exposure, connection establishment, as well as network structure. Thus, we propose a new Anonymous Online Social Network, which will facilitate a variety of monitoring and data analysis.

cs.SI↗

arXiv2014

Lifespan and propagation of information in On-line Social Networks a Case Study

Since 1950, information flows have been in the centre of scientific research. Up until internet penetration in the late 90s, these studies were based over traditional offline social networks. Several observations in offline information flows studies, such as two-step flow of communication and the importance of weak ties, were verified in several online studies, showing that the diffused information flows from one Online Social Network (OSN) to several others. Within that flow, information is shared to and reproduced by the users of each network. Furthermore, the original content is enhanced or weakened according to its topic, the dynamic and exposure of each OSNs. In such a concept, each OSN is considered a layer of information flows that interacts with each other. In this paper, we examine such flows in several social networks, as well as their diffusion and lifespan across multiple OSNs, in terms of user-generated content. Our results verify the perception of content and information connection in various OSNs.

cs.SI↗

arXiv2013

Real Time Enhanced Random Sampling of Online Social Networks

Social graphs can be easily extracted from Online Social Networks. However these networks are getting larger from day to day. Sampling methods used to evaluate graph information cannot accurately extract graph properties. Furthermore Social Networks are limiting the access to their data, making the crawling process even harder. A novel approach on Random Sampling is proposed, considering both limitation and resources. We evaluate this proposal with 4 different settings on 5 different Test Graphs, crawled directly from Twitter. Through comparing the results we observe the pros and cons of its method as well as their resource allocation. Concluding we present their best area of application.

cs.SI↗