The Disparity Between Scientific Consensus and American Public Opinion of Genetically Modified Organisms and Genetic Engineering

Genetically modified organisms (GMOs) and genetic engineering (GE) are accepted as safe and useful by the consensus of the scientific community. Their diverse utility has shown promise in addressing major challenges of the 21st century, including world hunger, global warming, and the prevalence of diet-related diseases (e.g. heart disease, cancer, diabetes, etc.). A 2014 Pew Research Center survey revealed that while 88% of scientists agreed that GM foods were safe to eat, only 37% of American consumers agreed. Furthermore, only 35% of U.S. adults trusted scientists to accurately inform the public about GMOs. To explain this disparity, I synthesize information about stakeholders in GMOs and GE, demographics linked to acceptance and denial, interpretation of scientific consensus, psychological mechanisms controlling bias, and poor practice of science. Analysis reveals that the disparity in GMO and GE perception between the scientific community and the American public was caused by bad science, foreign political agendas, profit-driven media, and psychological factors, such as intuitive expectations, soft attitudes, and the backfire effect; furthermore, I show that despite innate conduits for bias development, educated, high income, and youthful demographics will shrink the gap between scientific consensus and public opinion if GMO education and equal access to education increase

Combining social network analysis and sentiment analysis to explore the potential for online radicalisation

The increased online presence of jihadists has raised the possibility of individuals being radicalised via the Internet. To date, the study of violent radicalisation has focused on dedicated jihadist websites and forums. This may not be the ideal starting point for such research, as participants in these venues may be described as “already madeup minds”. Crawling a global social networking platform, such as YouTube, on the other hand, has the potential to unearth content and interaction aimed at radicalisation of those with little or no apparent prior interest in violent jihadism. This research explores whether such an approach is indeed fruitful. We collected a large dataset from a group within YouTube that we identified as potentially having a radicalising agenda. We analysed this data using social network analysis and sentiment analysis tools, examining the topics discussed and what the sentiment polarity (positive or negative) is towards these topics. In particular, we focus on gender differences in this group of users, suggesting most extreme and less tolerant views among female users

Quootstrap: Scalable Unsupervised Extraction of Quotation-Speaker Pairs from Large News Corpora via Bootstrapping

We propose Quootstrap, a method for extracting quotations, as well as the names of the speakers who uttered them, from large news corpora. Whereas prior work has addressed this problem primarily with supervised machine learning, our approach follows a fully unsupervised bootstrapping paradigm. It leverages the redundancy present in large news corpora, more precisely, the fact that the same quotation often appears across multiple news articles in slightly different contexts. Starting from a few seed patterns, such as ["Q", said S.], our method extracts a set of quotation-speaker pairs (Q, S), which are in turn used for discovering new patterns expressing the same quotations; the process is then repeated with the larger pattern set. Our algorithm is highly scalable, which we demonstrate by running it on the large ICWSM 2011 Spinn3r corpus. Validating our results against a crowdsourced ground truth, we obtain 90% precision at 40% recall using a single seed pattern, with significantly higher recall values for more frequently reported (and thus likely more interesting) quotations. Finally, we showcase the usefulness of our algorithm's output for computational social science by analyzing the sentiment expressed in our extracted quotations.Comment: Accepted at the 12th International Conference on Web and Social Media (ICWSM), 201

Cheap Sentiment

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