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

Computational Controversy

Climate change, vaccination, abortion, Trump: Many topics are surrounded by fierce controversies. The nature of such heated debates and their elements have been studied extensively in the social science literature. More recently, various computational approaches to controversy analysis have appeared, using new data sources such as Wikipedia, which help us now better understand these phenomena. However, compared to what social sciences have discovered about such debates, the existing computational approaches mostly focus on just a few of the many important aspects around the concept of controversies. In order to link the two strands, we provide and evaluate here a controversy model that is both, rooted in the findings of the social science literature and at the same time strongly linked to computational methods. We show how this model can lead to computational controversy analytics that have full coverage over all the crucial aspects that make up a controversy.Comment: In Proceedings of the 9th International Conference on Social Informatics (SocInfo) 201

Exploratory topic modeling with distributional semantics

As we continue to collect and store textual data in a multitude of domains, we are regularly confronted with material whose largely unknown thematic structure we want to uncover. With unsupervised, exploratory analysis, no prior knowledge about the content is required and highly open-ended tasks can be supported. In the past few years, probabilistic topic modeling has emerged as a popular approach to this problem. Nevertheless, the representation of the latent topics as aggregations of semi-coherent terms limits their interpretability and level of detail. This paper presents an alternative approach to topic modeling that maps topics as a network for exploration, based on distributional semantics using learned word vectors. From the granular level of terms and their semantic similarity relations global topic structures emerge as clustered regions and gradients of concepts. Moreover, the paper discusses the visual interactive representation of the topic map, which plays an important role in supporting its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent Data Analysis (IDA 2015

Journalistic practices of science popularization in the context of users’ agenda: A case study of „New Scientist”

The article includes a discussion of two models which describe contemporary communication processes in journalism: agenda-setting and news value, indicating the need to expand their research tools to include qualitative methods, and merging the analyses of the reception and the message. It also includes indications as to the possibility, or even the social relevance, of the methods for applying those research perspectives to analysing journalism popularising science. Later, I present the results of an analysis of the content of a sample of 500 most read popular science texts available on the New Scientist website. I demonstrate which thematic areas were valued by the readers, and what values are most commonly applied. Further, upon applying a filter in the form of surveys regarding reader preferences, I discuss the main linguistic devices utilised for controlling readers’ attention. The shaping of the hierarchy of importance of items of news is the result of a dynamic interaction between (1) the thematic priorities and discursive strategies of imposing elite representations of science within media agenda, and (2) the means of negotiating order and values of specific content, which are correlated with readers’ preferences, both in terms of the content and the form of providing popular scientific information

Ethical Implications of Predictive Risk Intelligence

open access articleThis paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. The pa-per covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews. Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelli-gence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as so-cial media sites. Also, there are issues relating to the transparency and accountabil-ity of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In re-sponse to these issues, the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Re-sponsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees. This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society