Hate is not Binary: Studying Abusive Behavior of #GamerGate on Twitter

Over the past few years, online bullying and aggression have become increasingly prominent, and manifested in many different forms on social media. However, there is little work analyzing the characteristics of abusive users and what distinguishes them from typical social media users. In this paper, we start addressing this gap by analyzing tweets containing a great large amount of abusiveness. We focus on a Twitter dataset revolving around the Gamergate controversy, which led to many incidents of cyberbullying and cyberaggression on various gaming and social media platforms. We study the properties of the users tweeting about Gamergate, the content they post, and the differences in their behavior compared to typical Twitter users. We find that while their tweets are often seemingly about aggressive and hateful subjects, "Gamergaters" do not exhibit common expressions of online anger, and in fact primarily differ from typical users in that their tweets are less joyful. They are also more engaged than typical Twitter users, which is an indication as to how and why this controversy is still ongoing. Surprisingly, we find that Gamergaters are less likely to be suspended by Twitter, thus we analyze their properties to identify differences from typical users and what may have led to their suspension. We perform an unsupervised machine learning analysis to detect clusters of users who, though currently active, could be considered for suspension since they exhibit similar behaviors with suspended users. Finally, we confirm the usefulness of our analyzed features by emulating the Twitter suspension mechanism with a supervised learning method, achieving very good precision and recall.Comment: In 28th ACM Conference on Hypertext and Social Media (ACM HyperText 2017

The Early Bird Catches The Term: Combining Twitter and News Data For Event Detection and Situational Awareness

Twitter updates now represent an enormous stream of information originating from a wide variety of formal and informal sources, much of which is relevant to real-world events. In this paper we adapt existing bio-surveillance algorithms to detect localised spikes in Twitter activity corresponding to real events with a high level of confidence. We then develop a methodology to automatically summarise these events, both by providing the tweets which fully describe the event and by linking to highly relevant news articles. We apply our methods to outbreaks of illness and events strongly affecting sentiment. In both case studies we are able to detect events verifiable by third party sources and produce high quality summaries

Comparison of boreal ecosystem model sensitivity to variability in climate and forest site parameters

Ecosystem models are useful tools for evaluating environmental controls on carbon and water cycles under past or future conditions. In this paper we compare annual carbon and water fluxes from nine boreal spruce forest ecosystem models in a series of sensitivity simulations. For each comparison, a single climate driver or forest site parameter was altered in a separate sensitivity run. Driver and parameter changes were prescribed principally to be large enough to identify and isolate any major differences in model responses, while also remaining within the range of variability that the boreal forest biome may be exposed to over a time period of several decades. The models simulated plant production, autotrophic and heterotrophic respiration, and evapotranspiration (ET) for a black spruce site in the boreal forest of central Canada (56°N). Results revealed that there were common model responses in gross primary production, plant respiration, and ET fluxes to prescribed changes in air temperature or surface irradiance and to decreased precipitation amounts. The models were also similar in their responses to variations in canopy leaf area, leaf nitrogen content, and surface organic layer thickness. The models had different sensitivities to certain parameters, namely the net primary production response to increased CO2 levels, and the response of soil microbial respiration to precipitation inputs and soil wetness. These differences can be explained by the type (or absence) of photosynthesis-CO2 response curves in the models and by response algorithms of litter and humus decomposition to drying effects in organic soils of the boreal spruce ecosystem. Differences in the couplings of photosynthesis and soil respiration to nitrogen availability may also explain divergent model responses. Sensitivity comparisons imply that past conditions of the ecosystem represented in the models\u27 initial standing wood and soil carbon pools, including historical climate patterns and the time since the last major disturbance, can be as important as potential climatic changes to prediction of the annual ecosystem carbon balance in this boreal spruce forest

BlogForever D2.4: Weblog spider prototype and associated methodology

The purpose of this document is to present the evaluation of different solutions for capturing blogs, established methodology and to describe the developed blog spider prototype

BlogForever D5.1: Design and Specification of Case Studies

This document presents the specification and design of six case studies for testing the BlogForever platform implementation process. The report explains the data collection plan where users of the repository will provide usability feedback through questionnaires as well as details of scalability analysis through the creation of specific log files analytics. The case studies will investigate the sustainability of the platform, that it meets potential users’ needs and that is has an important long term impact

Self-supervised automated wrapper generation for weblog data extraction

Data extraction from the web is notoriously hard. Of the types of resources available on the web, weblogs are becoming increasingly important due to the continued growth of the blogosphere, but remain poorly explored. Past approaches to data extraction from weblogs have often involved manual intervention and suffer from low scalability. This paper proposes a fully automated information extraction methodology based on the use of web feeds and processing of HTML. The approach includes a model for generating a wrapper that exploits web feeds for deriving a set of extraction rules automatically. Instead of performing a pairwise comparison between posts, the model matches the values of the web feeds against their corresponding HTML elements retrieved from multiple weblog posts. It adopts a probabilistic approach for deriving a set of rules and automating the process of wrapper generation. An evaluation of the model is conducted on a dataset of 2,393 posts and the results (92% accuracy) show that the proposed technique enables robust extraction of weblog properties and can be applied across the blogosphere for applications such as improved information retrieval and more robust web preservation initiatives

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