Computational report writing

Computational Modelling in the Humanities and Social Sciences The House of Commons SPARQL endpoint ([login to view URL]) provides access to structured data about Members of Parliament (MPs) and questions that have been asked in the House of Commons. Use this data together with data from Wikidata (where necessary) to answer the following questions, presenting your findings in the form of a report of up to 3000 words, based on data about questions asked by MPs between 1 January 2023 and 30 September 2023 inclusive: Q1. To what extent do Members of Parliament (MPs) tend to ask questions that directly reference their own constituency or a location in it? You should answer this question by identifying named entities that refer to places or identifiable geographical features (e.g. “Dartford Crossing”, “Reading Gaol”, etc.) in asked questions, and determining whether or not these are located in the MP’s constituency using data from Wikidata. You are free to choose any reasonable method in doing so – even if doing so will result in some false negatives – (e.g. relying on Wikidata property P131 “located in the administrative territorial entity”), however you should estimate how reliable you believe your chosen approach is. (N.B. it is not expected or required that your approach will result in 100% accuracy.) Q2. By applying LDA topic modeling and analyzing the results, what (if any) identifiable regional differences are there in the types of questions asked – e.g. do MPs representing, say, constituencies located in the North of England tend to ask more questions about certain topics than those in Southeast England? In answering this question, you should start by aggregating data into regions larger than an electoral district, such as those denoted by the property “region of England” [login to view URL] (For simplicity, you may treat Scotland and Northern Ireland as two separate regions without further subdivisions, or alternatively use any reasonable administrative subdivisions for these regions as you see fit). Discuss the assumptions and limitations of your approach and analysis. The following SPARQL query can be used as a starting point: SELECT * WHERE { ?question <[login to view URL]> ?qnum . ?person <[login to view URL]> ?question . ?question <[login to view URL]> ?text . ?question <[login to view URL]> ?date . FILTER (?date >= "2023-01-01+00:00"^^xsd:dateTime && ?date < "2023-10-01+00:00"^^xsd:dateTime) } This query returns the following data: ?question Entity representing a question ?person Entity representing the person who asked the question ?qnum Numerical identifier for this question ?text The text of the question ?date The date the question was asked Your report must: 1. Document how data was collected, and briefly state any relevant modules or toolkits that were used. If using SPARQL queries not contained in your code (other than the one provided), you must include these as separate files within your code submission. 2. Describe your implementation and the models constructed. 3. Summarize your results in appropriate ways, including use of suitable visualizations. 4. Critically evaluate the models. This must involve critical analysis of the adequacy of the modelling (e.g. what are the assumptions the models rely on, and do they all hold; are there factors or biases that might invalidate conclusions drawn?), but should also involve some comparison with external data and/or published social/political science research on relevant subjects. 5. Discuss what conclusions can be drawn from the