The Alt-Right and Global Information Warfare

The Alt-Right is a neo-fascist white supremacist movement that is involved in violent extremism and shows signs of engagement in extensive disinformation campaigns. Using social media data mining, this study develops a deeper understanding of such targeted disinformation campaigns and the ways they spread. It also adds to the available literature on the endogenous and exogenous influences within the US far right, as well as motivating factors that drive disinformation campaigns, such as geopolitical strategy. This study is to be taken as a preliminary analysis to indicate future methods and follow-on research that will help develop an integrated approach to understanding the strategies and associations of the modern fascist movement.Comment: Presented and published through IEEE 2019 Big Data Conferenc

Active learning in annotating micro-blogs dealing with e-reputation

Elections unleash strong political views on Twitter, but what do people really think about politics? Opinion and trend mining on micro blogs dealing with politics has recently attracted researchers in several fields including Information Retrieval and Machine Learning (ML). Since the performance of ML and Natural Language Processing (NLP) approaches are limited by the amount and quality of data available, one promising alternative for some tasks is the automatic propagation of expert annotations. This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i.e., representation, web reputation) of politicians. Our main focus is on the methodology followed to build an original annotated dataset expressing opinion from two French politicians over time. We therefore review state of the art NLP-based ML algorithms to automatically annotate tweets using a manual initiation step as bootstrap. This paper focuses on key issues about active learning while building a large annotated data set from noise. This will be introduced by human annotators, abundance of data and the label distribution across data and entities. In turn, we show that Twitter characteristics such as the author's name or hashtags can be considered as the bearing point to not only improve automatic systems for Opinion Mining (OM) and Topic Classification but also to reduce noise in human annotations. However, a later thorough analysis shows that reducing noise might induce the loss of crucial information.Comment: Journal of Interdisciplinary Methodologies and Issues in Science - Vol 3 - Contextualisation digitale - 201

Social media and sentiment in bioenergy consultation

Purpose: The push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organisations towards energy development projects. Design/methodology/approach: This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised, and illustrated using a sample of tweets containing the term ‘bioenergy’ Findings: Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results. Research limitations/implications: Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector. Originality/value: Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity

The Fact-Checking Universe in Spring 2012: An Overview

By almost any measure, the 2012 presidential race is shaping up to be the most fact-checked electoral contest in American history. Every new debate and campaign ad yields a blizzard of fact-checking from the new full-time fact-checkers, from traditional news outlets in print and broadcast, and from partisan political organizations of various stripes. And though fact-checking still peaks before elections it is now a year-round enterprise that challenges political claims beyond the campaign trail.This increasingly crowded and contentious landscape raises at least two fundamental questions. First, who counts as a legitimate fact-checker? The various kinds of fact-checking at work both inside and outside of journalism must be considered in light of their methods, their audiences, and their goals. And second, how effective are fact-checkers -- or how effective could they be -- in countering widespread misinformation in American political life? The success of the fact-checkers must be assessed in three related areas: changing people's minds, changing journalism, and changing the political conversation. Can fact-checking really stop a lie in its tracks? Can public figures be shamed into being more honest? Or has the damage been done by the time the fact-checkers intervene?This report reviews the shape of the fact-checking landscape today. It pays special attention to the divide between partisan and nonpartisan fact-checkers, and between fact-checking and conventional reporting. It then examines what we know and what we don't about the effectiveness of fact-checking, using the media footprint of various kinds of fact-checkers as an initial indicator of the influence these groups wield. Media analysis shows how political orientation limits fact-checkers' impact in public discourse

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse