Understanding the Contribution of Recommendation Algorithms on Misinformation Recommendation and Misinformation Dissemination on Social Networks

Social networks are a platform for individuals and organizations to connect with each other and inform, advertise, spread ideas, and ultimately influence opinions. These platforms have been known to propel misinformation. We argue that this could be compounded by the recommender algorithms that these platforms use to suggest items potentially...

Tweetology of Learning Analytics: What does Twitter tell us about the trends and development of the field?

Twitter is a very popular microblogging platform that has been actively used by scientific communities to exchange scientific information and to promote scholarly discussions. The present study aimed to leverage the tweet data to provide valuable insights into the development of the learning analytics field since its initial days. Descriptive...

Quantifying changes in societal optimism from online sentiment

Individuals can hold contrasting views about distinct times: for example, dread over tomorrow’s appointment and excitement about next summer’s vacation. Yet, psychological measures of optimism often assess only one time point or ask participants to generalize about their future. Here, we address these limitations by developing the optimism...

How do new transit stations affect people's sentiment and activity? A case study based on social media data in Hong Kong

Urban rail development can increase land value, reduce commute time, and increase accessibility, as reported in the literature. However, little is known about the impact of opening urban rail transit stations on people's sentiment, particularly in the context of large metropolises where population density is significantly high. This paper...

Detecting Rumors in Twitter for Humanitarian Activities

In this Master Thesis project, the objective is to study how can Supervised Machine Learning be used to detect text-based rumours for humanitarian activities in Twitter. A model was developed in this project in order to classify a tweet at question whether is a rumour or not and whether is relevant to humanitarian activities or not. The findings...

Public opinion on Twitter: A case study on palm oil

The theory of the Habermasian public sphere is applied to Twitter. To do so, the traditional literature on public opinion is combined with more recent literature on social media. After the literature research the main knowledge gap is identified. This gap is the lacking relation between the philosophical investigation on public opinion on...

Exploring Human Activity Patterns Across Cities through Social Media Data

Cities are complex systems, a particular dimension largely contributing to the complexity of cities is detected in the multiplicity of the behaviour of people who inhabit. In the urban context, these behaviours can be inferred from human activities. Understanding the distinctions of these activities between cities is crucial. Previous works...

Quantitative Prediction of Twitter Message Dissemination: A Machine Learning Approach

Predicting the popularity of contents in social networks is quite important for several applications such as viral marketing, news propagation and personalization. In this work, we developed an statistical learning approach to predict the popularity of tweets in the twitter social network. We extracted several user-based, tweet-based and network...

Tweet-Based Election Prediction

Twitter is a microblogging service that has more than 500 million messages on a daily basis. Scholars has been utilizing Twitter to monitor people reactions in political activities, such as debates and campaigns. By doing so, some of them claim that a forecast or prediction to an election can be made. Using the data from 2014 Indonesia...

Social Web Data Analytics: Relevance, Redundancy, Diversity

In the past decade, the Social Web has evolved into both an essential channel for people to exchange information and a new type of mass media. The immense amount of data produced presents new possibilities and challenges: algorithms and technologies need to be developed to extract and infer useful information from the Social Web. One of the main...

Business Intelligence from User Generated Content: Digging into microblog posts about new smartphones

High-tech and dynamic markets are faced with a rapidly changing environment and consumer requirements. At the same time, UGC is increasing drastically over the past years. UGC is becoming increasingly important in purchasing decisions while the business intelligence methods that leverage UGC are still in their infancy. A model that presents a...

Quantifying user influence on Sina Weibo

Microblog service has been becoming popular all over the world. The number of people owning a microblog account has been exponentially increasing during the last few years. With the development of microblog, more and more enterprises, media and political organizations open the microblog accounts. They make full use of the microblog platform to...

Detection of Duplicate Content on Twitter

Depending on the needs of a user, searching on Twitter streams has proven to be very useful for a wide range of applications. Twitter however, has large volumes of information available in the form of tweets, that are shared by mil- lions of users. We have observed that many of these tweets contain mutually redundant information and due to this,...

Exploiting Twitter to fulfill information needs during incidents

How can Twitter be exploited to fulfill information needs of users on Twitter during incidents? In this Thesis this question wil be investigated and a strategy to locate tweets which fulfill information needs will be introduced. First, techniques are proposed to automatically detect incidents given an unstructured incident data source and...