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![Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
Analizing parliaments through SNs.
Why?
Main challenge: source of information (nodes and
relationships)
Co-sponsorship. [Fow06]
Speeches. [TPL06]
Strong and weak ties. [Kir11]
Can we discover relationships involving third-party actors?
Third party discovery
Defining meaningful relationships.
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![Motivation. Related works. Generating bill based Policy Networks. Results Conclusions Future work
An overview of the literature.
Co-occurrence. [EESGGHAC14], [PSIO06].
Enriching links with the strength and semantics of relations.
[Tan07],[PSB07],[ZAR03].
Beyond document co-occurrence. [NCSS06],[Bra06].
A (very) related paper. [MID+13]
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DAMA - Final Presentation
- 1. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Mining parliamentary data and news articles to find patterns of collaboration between politicians and third party actors. Francisco Rodr´ıguez Drumond DAMA & LARCA - UPC July 7,2014 1 / 30
- 2. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Social Networks: a natural tool for political analysis. Nodes: Families of the political landscape of XV century Florence. Links: marriages between families (alliances). 2 / 30
- 3. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Analizing parliaments through SNs. Why? Main challenge: source of information (nodes and relationships) Co-sponsorship. [Fow06] Speeches. [TPL06] Strong and weak ties. [Kir11] Can we discover relationships involving third-party actors? Third party discovery Defining meaningful relationships. 3 / 30
- 4. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work An overview of our task 4 / 30
- 5. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work An overview of our task 5 / 30
- 6. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work An overview of our task 6 / 30
- 7. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work An overview of our task 7 / 30
- 8. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work SOPA: A motivating example. Policy Networks (PN): Social networks for political analysis. 8 / 30
- 9. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work An overview of the literature. Co-occurrence. [EESGGHAC14], [PSIO06]. Enriching links with the strength and semantics of relations. [Tan07],[PSB07],[ZAR03]. Beyond document co-occurrence. [NCSS06],[Bra06]. A (very) related paper. [MID+13] 9 / 30
- 10. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work A (very) related paper. Moschopoulos (2013) Toward the automatic extraction of policy networks using web links and documents Two pre-computed PNs: Ireland and Greece. Ground truth used for measuring correlations with similarity measures. Web based. Three types of similarity metrics: Co-occurrence metrics (Set comparisons). Text-based metrics. Link-based metrics. 10 / 30
- 11. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Generating bill based Policy Networks: the architecture. 11 / 30
- 12. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Finding news articles that talk about a bill. Topic modeling: TF-IDF for keyword extraction. One bill - one document. Whole set of bills as the corpus. 1,2,3-ngrams. Top 1000 keywords for each bill. Querying news articles: Bills and news articles modeled as vectors Cosine similarity for comparison. Rocchio’s rule for improving queries. 12 / 30
- 13. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Selecting relevant news articles. Threshold: point that maximizes: threshold = argmax p |p − (p.b )b | Intuition: point at which there is no significant gain in score. 13 / 30
- 14. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Entity extraction and preprocessing. MITIE for entity extraction + 1 Entity Normalization ‘The Univ. lumiere Lyon 2’ → ‘Univ Lumiere Lyon 2’ 2 Mapping organization initials to the whole name ‘The World Life Fund (WLF) has...’ → ‘World Life Fund’ = ‘WLF’ 3 Mapping partial names with full names ‘George Harrison preferred .... Harrison also...’ → ‘George Harrison’ = ‘Harrison’ 4 Expanding names based on the news corpus ‘Polit`ecnica de Catalunya’ → ‘Universitat Polit`ecnica de Catalunya’ 14 / 30
- 15. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Filtering relevant entities. Problem: +3000 entities per bill Noise. Expensive comparisons. Solution: Document co-occurrence + Latent Semantic Indexing (LSI) for fast similarity computation. Hierarchical Agglomerative Clustering (HAC) for grouping entities based on their similarity. Politicians → seed entities. Silhoutte for detecting the best cluster containing seed entities. 15 / 30
- 16. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Computing and thresholding entity similarities. Entities represented as vectors of 1...3-grams occurring in paragraphs they are mentioned in. TF-IDF with sublinear TF scaling (tf = 1 + log(frequency)) Cosine similarity for comparing the vectors. Elbows for detecting relevant entities for each entity. Two entities e1 and e2 are related iff they are in each others relevant entities list. 16 / 30
- 17. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Results. Two bills: BCN-World. Law of Popular Non-referendary Consults. Look at: Communities → colors. Influencers → node size. 17 / 30
- 18. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work BCN-World - Organizations. Acencas CUP Tripartito Canales Y Puertos De Tarragona Puerto De Tarragona Diputacion De Tarragona Camara De Comercio De Tarragona Pimec Govern PSC Parlament Ciu Veremonte ERC Icv-euia PP La Caixa Melco Hard Rock Cepta PPC Sociedad Centre Medics Selva Maresme Ciutadans Grup Peralada Camara Catalana AECE URV Value RetailFerrari Melia Hard Rock Cafe 18 / 30
- 19. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work BCN-World - Persons-Organizations. Felip Puig Francesc Xavier Mena Pimec Diputacion De Tarragona Govern Veremonte Josep Gonzalez Josep Poblet Pere Granados Xavier Adsera Salvador Guillermo Isidre Faine Xavier Pallares Francesc Homs Antoni Belmonte Cepta Dolors Llobet Caixabank Macia Alavedra Javier De La Rosa Daniel De Alfonso Hortensia GrauJoan Herrera Santi Vila Jordi VilajoanaLluis Rullan Melco Xavier Sabate Josep Felix Ballesteros Puerto De Tarragona Enrique Bañuelos Josep Andreu URV Isabel Vallet Joan Pons Icv-euia Pere Aragones Parlament Hard Rock Grup Peralada PSC PP Jordi Turull Marta Rovira Miquel Salazar Jordi Pons Jaume Amat Sociedad Centre Medics Selva Maresme Pere Navarro Andreu Mas-colell Caixa Damia Calvet Oriol Junqueras Artur Mas Camara Catalana Ciu ERC PPC Albert Batet Alicia Romero Melia Value Retail Alejandro Fernandez Enric Millo Alicia Sanchez-camacho CUP Enric Genesca Tripartito Ciutadans Agusti Colom Camara De Comercio De Tarragona Ferrari La Roca Ernest Maragall Josep Mayoral Carles Pellicer Acencas Francesc Perendreu AECE Jordi Sierra 19 / 30
- 20. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Law of Popular Non-referendary Consults. - Organizations. Manresadecideix UDC Comunidad Valenciana Senado Upyd Omnium Tribunal Constitucional Moviment Arenyenc PP Juzgado De Lo Contencioso Parlament Greenpeace Solidaritat ERC PSC Podemos PSOE Icv-euia Ciudadanos Congreso Ciu Compromis CDC CUP BNG Barcelona DecideixReagrupament Bildu ANC Els Verds 20 / 30
- 21. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Law of Popular Non-referendary Consults. - Persons. Lehendakari Ibarretxe Manuel Fraga Carles Mora Joan Ridao Uriel Bertran Felip Puig Jordi Fabrega Joan Saura Miquel Calcada Joan Puigcercos Lluis Corominas Jaume De Frontanya Jose Blanco Jose Manuel Durao Barroso Jose Luis Rodriguez Zapatero Mariano Rajoy Josep Maria Pelegri Josep Lluis Carod-roviraJosep Antoni Duran Jose Zaragoza Maria Emilia Casas Maria Mas Jose Montilla Leon Josep Pique Josep Camprubi Lauren Uria Ramon Torramade Jordi Pujol Josep Rull Pedro Sanchez Rodrigo Rato Javier Arenas Jose Maria Aznar Maria Dolores De Cospedal Carles MartiJordi Hereu Joan Clos Xavier Trias Jordi Portabella Joan Ferran Ricard Goma Ferran Mascarell Pasqual Maragall Franco Miquel Iceta Soraya Saenz De Santamaria Patxi Lopez Angel Acebes Pere Jover Marc Carrillo Eduardo Zaplana Oriol Pujol Dolors Camats Jordi Molto Laia Bonet Joan Botella Joana Ortega Joan Tarda Santiago Rodriguez Joan Herrera Ferran Requejo Abogado Del Estado Andreu Mumbru Artur Mas Assumpta Escarp Antoni Castells Jone Goyricelaya Ernest Benach Angel Ros Francesc Homs Jordi Ausas Nuria De Gispert Oriol Junqueras Albert Rivera Jordi Turull Marta Rovira Alicia Sanchez-camacho Ramon Espadaler Pere Navarro David Fernandez Maurici Lucena Alfredo Perez Rubalcaba Carme Forcadell Anna Simo Joan Ignasi ElenaJoan Rigol Esperanza Aguirre Jose Manuel Soria Laia Ortiz Carles Viver Pi-sunyer Rosa Diez Carme Garcia Josep Duran Lleida Alfred Morales Josep Maria Alvarez Muriel Casals Alfred Bosch Cayo Lara Pedro Arriola Jose Maria Mena Josep Maria Terricabras Dolors Batalla Arnaldo Otegi Joan Carles Gallego Alicia Sanchez Camacho Jose Domingo Angel Colom Carme Capdevila Joan Carretero Francesc Ribera Ahora Marti Ernest Maragall Jorge Fernandez Diaz Alfons Lopez Tena Alfonso Alonso Marina Llansana Marc Sanglas Alberto Ruiz Gallardon David Cameron Lluis Companys Carme Chacon Santi Vila Jose Antonio Perez Tapias Jordi Guillot Josep Felix Ballesteros Isabel Vallet Alberto Fernandez Diaz Antonio Hernando Cristobal Montoro Jaume Collboni Ban Ki-moon Ada Colau 21 / 30
- 22. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Conclusions. 1 An unbiased, low-cost, automated tool to aid the process of Policy Network generation and analysis. 2 The system automatically: 1 Detect entities related to a bill. 2 Computes and thresholds similarity measures for SN generation. 3 The method works better for finding relationships between organizations than for persons, particularly politicians. 22 / 30
- 23. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Contributions. 1 The use of bills as a cornerstone relating political actors, allowing to: Understand better the discovered relations. Find fine-grained relationships which would otherwise be missed. 2 A method for combining parliamentary open data and news papers for PN generation. 3 An unsupervised method for automatically detecting relevant entities of a given topic from a corpus of documents given a set of seed entities. 23 / 30
- 24. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Future work. 1 A more rigorous evaluation and problem definition. 2 Improving the PN generation phase. 3 Generative models. 4 Use-case driven PN generation. 5 Time component. 6 Signed Social Network Analysis 24 / 30
- 25. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work The end. Merci beacoup! Gr`acies! Grazie! Mult¸umesc! Questions? 25 / 30
- 26. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work Understanding the representation of entities and documents. 26 / 30
- 27. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work References I Roger B Bradford, Application of latent semantic indexing in generating graphs of terrorist networks, Intelligence and Security Informatics, Springer, 2006, pp. 674–675. Jes´us Espinal-Enr´ıquez, J Mario Siqueiros-Garc´ıa, Rodrigo Garc´ıa-Herrera, and Sergio Antonio Alcal´a-Corona, A literature-based approach to a narco-network, Social Informatics, Springer, 2014, pp. 97–101. James H Fowler, Connecting the congress: A study of cosponsorship networks, Political Analysis 14 (2006), no. 4, 456–487. Justin H Kirkland, The relational determinants of legislative outcomes: Strong and weak ties between legislators, The Journal of Politics 73 (2011), no. 03, 887–898. 27 / 30
- 28. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work References II Theodosis Moschopoulos, Elias Iosif, Leeda Demetropoulou, Alexandros Potamianos, and Shrikanth Shri Narayanan, Toward the automatic extraction of policy networks using web links and documents, Knowledge and Data Engineering, IEEE Transactions on 25 (2013), no. 10, 2404–2417. David Newman, Chaitanya Chemudugunta, Padhraic Smyth, and Mark Steyvers, Analyzing entities and topics in news articles using statistical topic models, Intelligence and Security Informatics, Springer, 2006, pp. 93–104. Bruno Pouliquen, Ralf Steinberger, and Clive Best, Automatic detection of quotations in multilingual news, Proceedings of Recent Advances in Natural Language Processing, 2007, pp. 487–492. 28 / 30
- 29. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work References III Bruno Pouliquen, Ralf Steinberger, Camelia Ignat, and Tamara Oellinger, arXiv preprint cs/0609066 (2006). Hristo Tanev, Unsupervised learning of social networks from a multiple-source news corpus, MuLTISOuRcE, MuLTILINguAL INfORMATION ExTRAc-TION ANd SuMMARIzATION (2007), 33. Matt Thomas, Bo Pang, and Lillian Lee, Get out the vote: Determining support or opposition from congressional floor-debate transcripts, Proceedings of the 2006 conference on empirical methods in natural language processing, Association for Computational Linguistics, 2006, pp. 327–335. 29 / 30
- 30. Motivation. Related works.Generating bill based Policy Networks. Results Conclusions Future work References IV Dmitry Zelenko, Chinatsu Aone, and Anthony Richardella, Kernel methods for relation extraction, The Journal of Machine Learning Research 3 (2003), 1083–1106. 30 / 30