Giannis Haralabopoulos

Giannis Haralabopoulos, Ioannis Anagnostopoulos

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.

Giannis Haralabopoulos, Elena Simperl

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.

Giannis Haralabopoulos, Ioannis Anagnostopoulos

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.