Stances identification in Political Discourses through Arguments Usage

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There is a huge need for structured political discussion: this thesis concentrates on finding a suitable approach to extract arguments used in politics to support or to oppose a certain position. Objective will be to develop a generic approach to analyse political debate transcripts/interviews and to characterize the embedded argumentative structure.The expected result is a small demonstrative graphical tool allowing a generic user to interact and make sense out of the knowledge structure extracted. Understanding and creating an informed opinion about facts of everyday life is becoming increasingly hard for users, due to the multiplicity of the sources and the crossing between traditional media and the user generated contents (UGC). This is particularly important in the context of elections, as citizens need to form an opinion to guide their voting based on partial coverage of subject in multiple interviews and debates.This thesis concentrates on finding a suitable approach to extract arguments used in politics to support or to oppose a certain position [4]. This implies extracting supposed relationships of causal nature from natural language [5-7].Method: the suggested approach is to understand and adapt the method used by “causenet” [1], thus employing pattern-based causal extraction from TV debates transcripts of the US presidential election (2020 [2] and 2016 [3]). Once validated the method, this should be applied to the Democratic presidential primary debates: by merging the political stance of each speaker, it is possible to find a view over the arguments used pro/against a specific subject, giving a fuzzy (membership degree) of pro/contra classes for each argument.Risks: there two main risks related to this proposal. 1) It is possible that the method used in causenet is unable to generalize for less structure data, such as transcript of dialogues. 2) The obtained results, in particular with respect to the argumentative structure into membership degrees, can be strongly affected by definition of the political stance, that is somehow subjective. This can result into a limited generalisation.Main objective of this work will be to develop a generic approach to analyse political debate transcripts/interviews and to characterize the embedded argumentative structure in term of pro and contra arguments. To simplify the context and offer a good coverage of multiple subjects, the benchmark will be performed using TV debates from US presidential elections (2020 and 2016). Subsequently, a fuzzy membership can be computed, using the stance of the politicians to obtain a broader characterization of general assertions adopted in the political argumentative panorama.The expected result is a small demonstrative (MVP) graphical tool allowing a generic user to interact and make sense out of the knowledge structure extracted. Useful features, such as the possibility for a user to input research keywords or subjects of interest should be included, together with filtering capabilities.BibliographyHeindorf, Stefan, Yan Scholten, Henning Wachsmuth, Axel-Cyrille Ngonga Ngomo, and Martin Potthast. "Causenet: Towards a causality graph extracted from the web." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 3023-3030. 2020. URL: https://papers.dice-research.org/2020/CIKM-20/heindorf_2020a_public.pdf https://causenet.org/https://www.kaggle.com/headsortails/us-election-2020-presidential-debateshttps://www.kaggle.com/scollins/presidential-debate-analysis/Taber, Charles S., and Milton Lodge. "Motivated skepticism in the evaluation of political beliefs." American journal of political science 50, no. 3 (2006): 755-769. DOI: 10.1111/j.1540-5907.2006.00214.x URL: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1540-5907.2006.00214.xSchank, Roger C. "Conceptual dependency: A theory of natural language understanding." Cognitive psychology 3, no. 4 (1972): 552-631. URL: https://scholarspace.manoa.hawaii.edu/handle/10125/50549Mueller, Roland M., and Sebastian Huettemann. "Extracting causal claims from information systems papers with natural language processing for theory ontology learning." In Proceedings of the 51st Hawaii international conference on system sciences. 2018. URL: https://scholarspace.manoa.hawaii.edu/bitstream/10125/50549/1/paper0662.pdfGirju, Roxana, and Dan I. Moldovan. "Text mining for causal relations." In FLAIRS conference, pp. 360-364. 2002. URL: https://www.aaai.org/Papers/FLAIRS/2002/FLAIRS02-071.pdf

Project Idea Metadata

• Project Idea Name: Stances identification in Political Discourses through Arguments Usage
• Date: 11/27/2021 8:35:37 AM
• Administrators:
  • Benjamin Emmenegger

Project Idea Description

Understanding and creating an informed opinion about facts of everyday life is becoming increasingly hard for users, due to the multiplicity of the sources and the crossing between traditional media and the user generated contents (UGC). This is particularly important in the context of elections, as citizens need to form an opinion to guide their voting based on partial coverage of subject in multiple interviews and debates.

This thesis concentrates on finding a suitable approach to extract arguments used in politics to support or to oppose a certain position [4]. This implies extracting supposed relationships of causal nature from natural language [5-7].

Method: the suggested approach is to understand and adapt the method used by “causenet” [1], thus employing pattern-based causal extraction from TV debates transcripts of the US presidential election (2020 [2] and 2016 [3]). Once validated the method, this should be applied to the Democratic presidential primary debates: by merging the political stance of each speaker, it is possible to find a view over the arguments used pro/against a specific subject, giving a fuzzy (membership degree) of pro/contra classes for each argument.

Risks: there two main risks related to this proposal. 1) It is possible that the method used in causenet is unable to generalize for less structure data, such as transcript of dialogues. 2) The obtained results, in particular with respect to the argumentative structure into membership degrees, can be strongly affected by definition of the political stance, that is somehow subjective. This can result into a limited generalisation.

Main objective of this work will be to develop a generic approach to analyse political debate transcripts/interviews and to characterize the embedded argumentative structure in term of pro and contra arguments. To simplify the context and offer a good coverage of multiple subjects, the benchmark will be performed using TV debates from US presidential elections (2020 and 2016). Subsequently, a fuzzy membership can be computed, using the stance of the politicians to obtain a broader characterization of general assertions adopted in the political argumentative panorama.

The expected result is a small demonstrative (MVP) graphical tool allowing a generic user to interact and make sense out of the knowledge structure extracted. Useful features, such as the possibility for a user to input research keywords or subjects of interest should be included, together with filtering capabilities.

Bibliography

1. Heindorf, Stefan, Yan Scholten, Henning Wachsmuth, Axel-Cyrille Ngonga Ngomo, and Martin Potthast. "Causenet: Towards a causality graph extracted from the web." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 3023-3030. 2020. URL: https://papers.dice-research.org/2020/CIKM-20/heindorf_2020a_public.pdf https://causenet.org/
2. https://www.kaggle.com/headsortails/us-election-2020-presidential-debates
3. https://www.kaggle.com/scollins/presidential-debate-analysis/
4. Taber, Charles S., and Milton Lodge. "Motivated skepticism in the evaluation of political beliefs." American journal of political science 50, no. 3 (2006): 755-769. DOI: 10.1111/j.1540-5907.2006.00214.x URL: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1540-5907.2006.00214.x
5. Schank, Roger C. "Conceptual dependency: A theory of natural language understanding." Cognitive psychology 3, no. 4 (1972): 552-631. URL: https://scholarspace.manoa.hawaii.edu/handle/10125/50549
6. Mueller, Roland M., and Sebastian Huettemann. "Extracting causal claims from information systems papers with natural language processing for theory ontology learning." In Proceedings of the 51st Hawaii international conference on system sciences. 2018. URL: https://scholarspace.manoa.hawaii.edu/bitstream/10125/50549/1/paper0662.pdf
7. Girju, Roxana, and Dan I. Moldovan. "Text mining for causal relations." In FLAIRS conference, pp. 360-364. 2002. URL: https://www.aaai.org/Papers/FLAIRS/2002/FLAIRS02-071.pdf

There is a huge need for structured political discussion: this thesis concentrates on finding a suitable approach to extract arguments used in politics to support or to oppose a certain position.

Objective will be to develop a generic approach to analyse political debate transcripts/interviews and to characterize the embedded argumentative structure.

The expected result is a small demonstrative graphical tool allowing a generic user to interact and make sense out of the knowledge structure extracted.