Imagined communities: Reflections on the origin and spread of nationalism

https://link.springer.com/content/pdf/10.2307%2F3343168.pdf

Development as Freedom

Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases.

/pdf/recursive-deep-models-for-semantic-compositionality-over-a-35h7g7u52l.pdf

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

The Structural Transformation of the Public Sphere: An Inquiry into a Category of Bourgeois Society

We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it.

The spread of true and false news online

This paper describes a system for real-time analysis of public sentiment toward presidential candidates in the 2012 U.S. election as expressed on Twitter, a micro-blogging service. Twitter has become a central site where people express their opinions and views on political parties and candidates. Emerging events or news are often followed almost instantly by a burst in Twitter volume, providing a unique opportunity to gauge the relation between expressed public sentiment and electoral events. In addition, sentiment analysis can help explore how these events affect public opinion. While traditional content analysis takes days or weeks to complete, the system demonstrated here analyzes sentiment in the entire Twitter traffic about the election, delivering results instantly and continuously. It offers the public, the media, politicians and scholars a new and timely perspective on the dynamics of the electoral process and public opinion.

A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle

Social media can be a double-edged sword for political misinformation, either a conduit propagating false rumors through a large population or an effective tool to challenge misinformation. To understand this phenomenon, we tracked a comprehensive collection of political rumors on Twitter during the 2012 US presidential election campaign, analyzing a large set of rumor tweets (n = 330,538). We found that Twitter helped rumor spreaders circulate false information within homophilous follower networks, but seldom functioned as a self-correcting marketplace of ideas. Rumor spreaders formed strong partisan structures in which core groups of users selectively transmitted negative rumors about opposing candidates. Yet, rumor rejecters neither formed a sizable community nor exhibited a partisan structure. While in general rumors resisted debunking by professional fact-checking sites (e.g. Snopes), this was less true of rumors originating with satirical sources.

/pdf/political-rumoring-on-twitter-during-the-2012-us-1otp22elkh.pdf

Political rumoring on Twitter during the 2012 US presidential election: Rumor diffusion and correction

This research elaborated and empirically tested the individual action component of the collective action model as applied to individual contributions to organizational information commons. The model extended prior theory and research by making six elaborations on the classic collective action model based on unique characteristics of information goods compared to material collective goods. The structural equation model was tested via LISREL analyses of data provided by 781 respondents in three high-tech firms who had access to corporate intranets as shared information goods. The results were highly similar across organizations and indicated that (a) level of production, information retrieval, and cost predicted the perceived value of information, (b) information value and cost predicted gain, and (c) information retrieval and gain predicted the level of individual contributions to the commons.

A Test of the Individual Action Model for Organizational

A Test of the Individual Action Model for Organizational Information Commons

This paper explores three broad questions about municipal Wi-Fi networks in the U.S.: why are cities getting involved, how do they go about deploying these networks, and what policy issues does this new trend raise? To explain municipal involvement, the paper points out that cities have both the means to provide relatively inexpensive deployment and the motives to provide wireless connectivity to city employees, foster the economic development of communities and offer universal and affordable broadband services to residents. The paper then explores nine possible business models, ordered according to two questions: who owns the network and who operates it. Each of the possible business models is described and its policy implications are discussed. Finally, the paper addresses the political and legal fight over the right of cities to build these networks. The authors argue in conclusion that the current municipal Wi-Fi movement should be allowed to proceed without federal restrictions.

François Bar

Papers

A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle

Political rumoring on Twitter during the 2012 US presidential election: Rumor diffusion and correction

A Test of the Individual Action Model for Organizational

A Test of the Individual Action Model for Organizational Information Commons

Municipal Wi-Fi Networks: The Goals, Practices, and Policy Implications of the U.S. Case (*)