Maria Giatsoglou

Sentiment analysis leveraging emotions and word embeddings

Abstract Sentiment analysis and opinion mining are valuable for extraction of useful subjective information out of text documents. These tasks have become of great importance, especially for business and marketing professionals, since online posted products and services reviews impact markets and consumers shifts. This work is motivated by the fact that automating retrieval and detection of sentiments expressed for certain products and services embeds complex processes and pose research challenges, due to the textual phenomena and the language specific expression variations. This paper proposes a fast, flexible, generic methodology for sentiment detection out of textual snippets which express people’s opinions in different languages. The proposed methodology adopts a machine learning approach with which textual documents are represented by vectors and are used for training a polarity classification model. Several documents’ vector representation approaches have been studied, including lexicon-based, word embedding-based and hybrid vectorizations. The competence of these feature representations for the sentiment classification task is assessed through experiments on four datasets containing online user reviews in both Greek and English languages, in order to represent high and weak inflection language groups. The proposed methodology requires minimal computational resources, thus, it might have impact in real world scenarios where limited resources is the case.

Capturing Social Data Evolution Using Graph Clustering

The fast and unpredictable evolution of social data poses challenges for capturing user activities and complex associations. Evolving social graph clustering promises to uncover the dynamics of latent user and content patterns. This Web extra overviews evolving data clustering approaches.

Retweeting Activity on Twitter: Signs of Deception

Given the re-broadcasts (i.e. retweets) of posts in Twitter, how can we spot fake from genuine user reactions? What will be the tell-tale sign — the connectivity of retweeters, their relative timing, or something else? High retweet activity indicates influential users, and can be monetized. Hence, there are strong incentives for fraudulent users to artificially boost their retweets’ volume. Here, we explore the identification of fraudulent and genuine retweet threads. Our main contributions are: (a) the discovery of patterns that fraudulent activity seems to follow (the “triangles ” and “homogeneity ” patterns, the formation of micro-clusters in appropriate feature spaces); and (b) “RTGen ”, a realistic generator that mimics the behaviors of both honest and fraudulent users. We present experiments on a dataset of more than 6 million retweets crawled from Twitter.

Sensors talk and humans sense Towards a reciprocal collective awareness smart city framework

Smart city infrastructures provide unique opportunities for innovative applications developing and testing. Sensor city installations offer the ground for experimenting with user-oriented services, which at the same time can test and improve the infrastructure itself. The proposed work summarizes principles and methodology for and experiment, entitled SEN2SOC which will bridge sensor measurements and social networks interactions via natural language generation for supporting smart city services. SEN2SOC aims at exploiting the SmartSantander infrastructure in a sensor to social reciprocal fashion such that the sensor measurements will be and communicated to the public (citizens, authorities, etc), while social networks users activities in relevance to sensors social postings will be analyzed and summarized both to verify sensors reporting and to develop collective aware applications.

ND-SYNC: Detecting Synchronized Fraud Activities

Given the retweeting activity for the posts of several Twitter users, how can we distinguish organic activity from spammy retweets by paid followers to boost a post’s appearance of popularity? More generally, given groups of observations, can we spot strange groups? Our main intuition is that organic behavior has more variability, while fraudulent behavior, like retweets by botnet members, is more synchronized. We refer to the detection of such synchronized observations as the Synchonization Fraud problem, and we study a specific instance of it, Retweet Fraud Detection, manifested in Twitter. Here, we propose: (A) ND-Sync, an efficient method for detecting group fraud, and (B) a set of carefully designed features for characterizing retweet threads. ND-Sync is effective in spotting retweet fraudsters, robust to different types of abnormal activity, and adaptable as it can easily incorporate additional features. Our method achieves a 97% accuracy on a real dataset of 12 million retweets crawled from Twitter.

Community Detection in Social Media by Leveraging Interactions and Intensities

Communities’ identification in topic-focused social media users interaction networks can offer improved understanding of different opinions and interest expressed on a topic. In this paper we present a community detection approach for user interaction networks which exploits both their structural properties and intensity patterns. The proposed approach builds on existing graph clustering methods that identify both communities of nodes, as well as outliers. The importance of incorporating interactions’ intensity in the community detection algorithm is initially investigated by a benchmarking process on synthetic graphs. By applying the proposed approach on a topic-focused dataset of Twitter users’ interactions, we reveal communities with different features which are further analyzed to reveal and summarize the given topic’s impact on social media users.

Requirements and architecture design principles for a smart city experiment with sensor and social networks integration

Smart city infrastructures offer unique testbeds ground for innovative experimentation and services offering. Sensors networks in cities with integrated social networks activities can improve people-centric services, while improving infrastructures setting. This work summarizes the principles and priorities chosen in a smart city experiment, entitled SEN2SOC which bridges sensor measurements and social networks interactions for supporting smart city services. SEN2SOC prioritizes requirements along particular categories which cover data collection, users sensing along with applications implementation and architectural concerns. These requirements are correlated with the suggested components in an architecture which is flexible enough in order to permit various activities control flow in terms of data preprocessing, conditions detection, statistical analysis as well as applications development and social data mining.

Massive Graph Management for the Web and Web 2.0

The problem of efficiently managing massive datasets has gained increasing attention due to the availability of a plethora of data from various sources, such as the Web. Moreover, Web 2.0 applications seem to be one of the most fruitful sources of information as they have attracted the interest of a large number of users that are eager to contribute to the creation of new data, available online. Several Web 2.0 applications incorporate Social Tagging features, allowing users to upload and tag sets of online resources. This activity produces massive amounts of data on a daily basis, which can be represented by a tripartite graph structure that connects users, resources and tags. The analysis of Social Tagging Systems (STS) emerges as a promising research field, enabling the identification of common patterns in the behavior of users, or the identification of communities of semantically related tags and resources, and much more. The massive size of STS datasets dictates the necessity for a robust underlying infrastructure to be used for their storage and access.

User communities evolution in microblogs: A public awareness barometer for real world events

In social media, users’ interactions are affected by real-world events which influence emergence and shifts of opinions and topics. Interactions around an event-related topic can be captured in a weighted network, while identification of connectivity and intensity patterns can improve understanding of users’ interest on the topic. Community detection is studied here as a means to reveal groups of social media users with common interaction patterns in such networks. The proposed community detection approach identifies communities exploiting both structural properties and intensity patterns, while dynamics of communities’ evolution around an event are revealed based on an iterative community detection and mapping scheme. We investigate the importance of considering interactions’ intensity for community detection via a benchmarking process on synthetic graphs and propose a generic framework for: i) modeling user interactions, ii) identifying static and evolving communities around events, iii) extracting quantitative and qualitative measurements from the communities’ timeline, iv) leveraging measurements to understand the events’ impact. Two real-world case studies based on Twitter interactions demonstrate the framework’s potential for capturing and interpreting associations among communities and events.

Dynamic Code Generation for Cultural Content Management

Digital repositories are popular means for preserving, restoring, and indexing archaeological and cultural content. They provide the base for development of a fauna of related applications including virtual tours and data management. Common difficulties such as the ever changing software specifications from domain experts make this task challenging as the alterations of the database schema lead to massive code rewrites. Within this context we propose and implement in practice a model for automated code generation building essentially a content management application by traversing a custom tree-based ERschema.