Argumentation Mining in Parliamentary Discourse

In parliamentary discourse, politicians expound their beliefs and goals through argumentation, and, to persuade the audience, they communicate their values by highlighting some aspect of an issue, an action which is commonly known as framing. The choices of frames are typically dependent upon the speaker’s ideology. In this proposed doctoral work, we will computationally analyze framing strategies and present a model for discovering the latent structure of framing of real-world issues in Canadian parliamentary discourse

Neural Based Statement Classification for Biased Language

Biased language commonly occurs around topics which are of controversial nature, thus, stirring disagreement between the different involved parties of a discussion. This is due to the fact that for language and its use, specifically, the understanding and use of phrases, the stances are cohesive within the particular groups. However, such cohesiveness does not hold across groups. In collaborative environments or environments where impartial language is desired (e.g. Wikipedia, news media), statements and the language therein should represent equally the involved parties and be neutrally phrased. Biased language is introduced through the presence of inflammatory words or phrases, or statements that may be incorrect or one-sided, thus violating such consensus. In this work, we focus on the specific case of phrasing bias, which may be introduced through specific inflammatory words or phrases in a statement. For this purpose, we propose an approach that relies on a recurrent neural networks in order to capture the inter-dependencies between words in a phrase that introduced bias. We perform a thorough experimental evaluation, where we show the advantages of a neural based approach over competitors that rely on word lexicons and other hand-crafted features in detecting biased language. We are able to distinguish biased statements with a precision of P=0.92, thus significantly outperforming baseline models with an improvement of over 30%. Finally, we release the largest corpus of statements annotated for biased language.Comment: The Twelfth ACM International Conference on Web Search and Data Mining, February 11--15, 2019, Melbourne, VIC, Australi

Nebraska Politics and the Environment: Framing Political Communication in the State of Nebraska in Comparison to National Level Discourse

Environmental public policy has seen little change on the national level in recent decades due to Congressional gridlock. Politicians on both sides of the aisle have entrenched their opposing viewpoints, and their communication on the topic utilizes issue frames to help sway the public to see their side. On the Republican side, these issue frames take the form of the “scientific uncertainty” and “economic consequences” frames. This study, based on issue framing, surveys the communication of Nebraska’s Republican State Senators to determine if they utilize the same issue frames or if they diverge from their national counterparts. By analyzing recent statements on environmental issues made by these State Senators, the main finding of this paper is that Nebraska’s State Senators used different issue frames than national Republicans over half of the time. Additionally, the statements made which adhered to the national issue frames were almost all issued by only two State Senators. The analysis performed as part of this paper supports the conclusion the majority of Nebraska’s Republican State Senators discuss environmental issues in a manner that differs from Republicans at the national level

Detecting Political Framing Shifts and the Adversarial Phrases within\\ Rival Factions and Ranking Temporal Snapshot Contents in Social Media

abstract: Social Computing is an area of computer science concerned with dynamics of communities and cultures, created through computer-mediated social interaction. Various social media platforms, such as social network services and microblogging, enable users to come together and create social movements expressing their opinions on diverse sets of issues, events, complaints, grievances, and goals. Methods for monitoring and summarizing these types of sociopolitical trends, its leaders and followers, messages, and dynamics are needed. In this dissertation, a framework comprising of community and content-based computational methods is presented to provide insights for multilingual and noisy political social media content. First, a model is developed to predict the emergence of viral hashtag breakouts, using network features. Next, another model is developed to detect and compare individual and organizational accounts, by using a set of domain and language-independent features. The third model exposes contentious issues, driving reactionary dynamics between opposing camps. The fourth model develops community detection and visualization methods to reveal underlying dynamics and key messages that drive dynamics. The final model presents a use case methodology for detecting and monitoring foreign influence, wherein a state actor and news media under its control attempt to shift public opinion by framing information to support multiple adversarial narratives that facilitate their goals. In each case, a discussion of novel aspects and contributions of the models is presented, as well as quantitative and qualitative evaluations. An analysis of multiple conflict situations will be conducted, covering areas in the UK, Bangladesh, Libya and the Ukraine where adversarial framing lead to polarization, declines in social cohesion, social unrest, and even civil wars (e.g., Libya and the Ukraine).Dissertation/ThesisDoctoral Dissertation Computer Science 201

Testing and Comparing Computational Approaches for Identifying the Language of Framing in Political News

This archive includes three files. BaumerEtAl_NAACL-HLT-2015.sql contains the data. _Readme.txt provides a description of the data format and instructions for use. naaclhlt2015-Framing.pdf is a preprint of the article that analyzes these data.These data were collected from Mechanical Turk workers (www.mturk.com) and students at two research universities who were asked to highlight the words and phrases related to framing in political news articles.US National Science Foundation Grant #ISS-111093