From: Kaylea Champion Date: Sat, 28 Mar 2020 21:17:21 +0000 (-0700) Subject: Merge pull request #5 from kayleachampion/master X-Git-Url: https://code.communitydata.science/covid19.git/commitdiff_plain/033149776c8bebf2a566c36d4d4b0d3ab8c9c48f?hp=dd7d968bb681dda6fed495e0cd437c0932961519 Merge pull request #5 from kayleachampion/master view data --- diff --git a/README.md b/README.md index ddb2b1e..10a868b 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # COVID-19 Digital Observatory -The COVID-19 Digital Observatory collects, aggregates, and distributes data from social media, search engine results, and Wikipedia to support social and data science research related to the pandemic. +The COVID-19 Digital Observatory collects, aggregates, and distributes data from social media, search engine results, and Wikipedia to support immediate public health response and social and data science research related to the pandemic. The [community data science collective](https://wiki.communitydata.science/Main_Page "The community data science collective wiki") is the early stages of building this project. We expect to make rapid progess and to begin releasing code and data soon. diff --git a/transliterations/README.md b/transliterations/README.md new file mode 100644 index 0000000..18df219 --- /dev/null +++ b/transliterations/README.md @@ -0,0 +1,3 @@ +# Transliterations + +This part of the project collects tranliterations of key phrases related to COVID-19 using Wikidata. We search the Wikidata API for entities in `src/wikidata_search.py` and then we make simple SPARQL queries in `src/wikidata_transliterations.py` to collect labels and aliases the entities. The labels come with language metadata. This seems to provide a decent initial list of relevant terms across multiple languages. diff --git a/transliterations/analysis/output/top5_queries_per_term_per_date.csv b/transliterations/analysis/output/top5_queries_per_term_per_date.csv new file mode 100644 index 0000000..5ce1aa7 --- /dev/null +++ b/transliterations/analysis/output/top5_queries_per_term_per_date.csv @@ -0,0 +1,6 @@ +"term","date","query.1","query.2","query.3","query.4","query.5" +"coronavirus","2020-03-27",coronavirus update,corona,coronavirus symptoms,news coronavirus,coronavirus cases +"covid-19","2020-03-27",covid-19 coronavirus,coronavirus,covid,covid-19 cases,covid 19 +"covid-19 pandemic","2020-03-27",coronavirus,covid-19 coronavirus pandemic,coronavirus pandemic,who,is covid-19 a pandemic +"covid19","2020-03-27",covid,covid 19,coronavirus covid19,coronavirus,covid19 cases +"sars-cov-2","2020-03-27",coronavirus,coronavirus sars-cov-2,covid-19,covid-19 sars-cov-2,sars diff --git a/transliterations/analysis/related_searches_example.R b/transliterations/analysis/related_searches_example.R new file mode 100644 index 0000000..2576d4a --- /dev/null +++ b/transliterations/analysis/related_searches_example.R @@ -0,0 +1,28 @@ +### COVID-19 Digital Observatory +### 2020-03-28 +### +### Minimal example analysis file using trending search data + +### Identify data source directory and file +DataDir <- ("../data/output/") +DataFile <- ("related_searches_top.csv") + +### Import and cleanup data +related.searches.top <- read.table(paste(DataDir,DataFile, + sep=""), + sep=",", header=TRUE, + stringsAsFactors=FALSE) + +### Aggregate top 5 search queries by term/day +top5.per.term.date <- aggregate(query ~ term + date, + data=related.searches.top, + head, 5) + +## Might cleanup a bit for further analysis or visualization... +top5.per.term.date$date <- as.Date(top5.per.term.date$date) + +### Export +write.table(top5.per.term.date, + file="output/top5_queries_per_term_per_date.csv", sep=",", + row.names=FALSE) + diff --git a/transliterations/data/input/base_terms.txt b/transliterations/data/input/base_terms.txt new file mode 100644 index 0000000..2d5f865 --- /dev/null +++ b/transliterations/data/input/base_terms.txt @@ -0,0 +1,6 @@ +coronavirus +covid-19 +covid19 +sars-cov-2 +covid-19 pandemic +sars-cov-2 pandemic diff --git a/transliterations/data/output/2020-03-27_wikidata_entity_labels.csv b/transliterations/data/output/2020-03-27_wikidata_entity_labels.csv new file mode 100644 index 0000000..035827b --- /dev/null +++ b/transliterations/data/output/2020-03-27_wikidata_entity_labels.csv @@ -0,0 +1,9300 @@ +entityid,label,langcode,is_alt +http://www.wikidata.org/entity/Q15089,província d'Itàlia,ca,False +http://www.wikidata.org/entity/Q15089,Italienische Provinz,de,False +http://www.wikidata.org/entity/Q15089,επαρχία της Ιταλίας,el,False +http://www.wikidata.org/entity/Q15089,province of Italy,en,False 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+covid-19 symptoms,Q87369504,86603314,1,2020-03-28T21:01:30Z +covid-19 updates,Q87461514,86691755,0,2020-03-28T21:01:31Z +covid-19 updates,Q87223238,86459413,1,2020-03-28T21:01:31Z diff --git a/transliterations/requirements.txt b/transliterations/requirements.txt new file mode 100644 index 0000000..46b2967 --- /dev/null +++ b/transliterations/requirements.txt @@ -0,0 +1,2 @@ +pytrends>=4.7.2 +mwapi>=0.5.1 diff --git a/transliterations/src/__init__.py b/transliterations/src/__init__.py new file mode 100644 index 0000000..5211ac6 --- /dev/null +++ b/transliterations/src/__init__.py @@ -0,0 +1,2 @@ +from wikidata_api_calls import * +from find_entities import * diff --git a/transliterations/src/collect_trends.py b/transliterations/src/collect_trends.py new file mode 100644 index 0000000..16a5c93 --- /dev/null +++ b/transliterations/src/collect_trends.py @@ -0,0 +1,76 @@ +# this follows a similar approach to nick's trends.js but in python +from pytrends.request import TrendReq +from datetime import datetime +from os import path +import csv +from itertools import islice, chain, zip_longest +import pandas as pd + + +# from itertools recipes +#https://docs.python.org/3.6/library/itertools.html#itertools-recipes +def grouper(iterable, n, fillvalue=None): + "Collect data into fixed-length chunks or blocks" + # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx" + args = [iter(iterable)] * n + return zip_longest(*args, fillvalue=fillvalue) + +def get_daily_trends(): + trendReq = TrendReq(backoff_factor=0.2) + today_trending = trendReq.today_searches() + daily_trends_outfile = path.join("..","data","output","daily_google_trends.csv") + + write_header = False + header = ['date','term','top'] + + if not path.exists(daily_trends_outfile): + write_header = True + + with open("../data/output/daily_google_trends.csv",'a',newline='') as of: + writer = csv.writer(of) + if write_header: + writer.writerow(header) + + for i, trend in enumerate(today_trending): + writer.writerow([str(datetime.now().date()),trend,i]) + +def get_related_queries(stems): + # we have to batch these in sets of 5 + trendReq = TrendReq(backoff_factor=0.2) + def _get_related_queries(chunk): + kw_list = list(filter(lambda x: x is not None, chunk)) + trendReq.build_payload(kw_list=kw_list) + related_queries = trendReq.related_queries() + for term, results in related_queries.items(): + for key, df in results.items(): + if df is not None: + df["term"] = term + yield (key,df) + + l = chain(*map(_get_related_queries, grouper(stems,5))) + out = {} + for key, value in l: + if key in out: + out[key].append(value) + else: + out[key] = [value] + + for k in out.keys(): + df = pd.concat(out[k]) + df['date'] = str(datetime.now().date()) + out[k] = df + outfile = path.join('..','data','output',f"related_searches_{k}.csv") + if path.exists(outfile): + mode = 'a' + header = False + else: + mode = 'w' + header = True + + df.to_csv(outfile, mode=mode, header=header,index=False) + +stems = [t.strip() for t in open("../data/input/base_terms.txt",'r')] + +get_daily_trends() + +get_related_queries(stems) diff --git a/transliterations/src/compile_transliterated_phrases.sh b/transliterations/src/compile_transliterated_phrases.sh new file mode 100755 index 0000000..55fe211 --- /dev/null +++ b/transliterations/src/compile_transliterated_phrases.sh @@ -0,0 +1,16 @@ +#!/bin/bash + +# For now these scripts don't accept command line arguments. It's an MVP + +echo "Reading Google trends" +python3 collect_trends.py + +echo "Searching for Wikidata entities using base_terms.txt" +python3 wikidata_search.py ../data/input/base_terms.txt --output ../data/output/wikidata_search_results.csv + +echo "Searching for Wikidata entities using Google trends" +python3 wikidata_search.py ../data/output/related_searches_rising.csv ../data/output/related_searches_top.csv --use-gtrends --output ../data/output/wikidata_search_results_from_gtrends.csv + +echo "Finding transliterations from Wikidata using sparql" +python3 wikidata_transliterations.py ../data/output/wikidata_search_results_from_gtrends.csv ../data/output/wikidata_search_results.csv --topN 10 20 --output ../data/output/$(date '+%Y-%m-%d')_wikidata_entity_labels.csv + diff --git a/transliterations/src/defaults.py b/transliterations/src/defaults.py new file mode 100644 index 0000000..3d6168f --- /dev/null +++ b/transliterations/src/defaults.py @@ -0,0 +1 @@ +user_agent = "COVID-19 Digital Observatory, a Community Data Science Collective project. (https://github.com/CommunityDataScienceCollective/COVID-19_Digital_Observatory)" diff --git a/transliterations/src/wikidata_api_calls.py b/transliterations/src/wikidata_api_calls.py new file mode 100644 index 0000000..212ed4f --- /dev/null +++ b/transliterations/src/wikidata_api_calls.py @@ -0,0 +1,35 @@ +# File defines functions for making api calls to find translations and transliterations for key terms. +import mwapi +import requests +import sys +import time +from defaults import user_agent + +def get_wikidata_api(): + session = mwapi.Session(host="https://wikidata.org/w/api.php", user_agent=user_agent) + return session + +def search_wikidata(session, term, *args, **kwargs): + search_results = session.get(action='query', + list='search', + srsearch=term, +# srqiprofile='popular_inclinks_pv', + srlimit='max', + srnamespace=0, + *args, + **kwargs) + + + query = search_results.get('query', None) + results = query.get('search', None) + + if results is None: + raise mwapi.session.APIError(f"No results for query: {term}") + + return results + +def run_sparql_query(q): + results = requests.get("https://query.wikidata.org/bigdata/namespace/wdq/sparql",params={"format":"json","query":q}) + time.sleep(2) + return results + diff --git a/transliterations/src/wikidata_search.py b/transliterations/src/wikidata_search.py new file mode 100644 index 0000000..a3abbc0 --- /dev/null +++ b/transliterations/src/wikidata_search.py @@ -0,0 +1,95 @@ +# generate a list of wikidata entities related to keywords +from os import path +from sys import stdout +from wikidata_api_calls import search_wikidata, get_wikidata_api +import csv +from itertools import chain + +class Wikidata_ResultSet: + def __init__(self): + self.results = [] + + def extend(self, term, results): + self.results.append( + (Wikidata_Result(term, result, i) + for i, result in enumerate(results)) + ) + + def to_csv(self, outfile=None, mode='w'): + if outfile is None: + of = stdout + + else: + if path.exists(outfile) and mode != 'w': + of = open(outfile,'a',newline='') + else: + of = open(outfile,'w',newline='') + writer = csv.writer(of) + writer.writerow(Wikidata_Result.__slots__) + writer.writerows(map(Wikidata_Result.to_list, chain(* self.results))) + + +class Wikidata_Result: + # store unique entities found in the search results, the position in the search result, and the date + __slots__=['search_term','entityid','pageid','search_position','timestamp'] + + def __init__(self, + term, + search_result, + position): + + self.search_term = term.strip() + self.entityid = search_result['title'] + self.pageid = int(search_result['pageid']) + self.search_position = int(position) + self.timestamp = search_result['timestamp'] + + def to_list(self): + return [self.search_term, + self.entityid, + self.pageid, + self.search_position, + self.timestamp] + +def run_wikidata_searches(terms): + api = get_wikidata_api() + resultset = Wikidata_ResultSet() + for term in terms: + search_results = search_wikidata(api, term) + resultset.extend(term, search_results) + return resultset + +def read_google_trends_files(terms_files): + def _read_file(infile): + return csv.DictReader(open(infile,'r',newline='')) + + for row in chain(* [_read_file(terms_file) for terms_file in terms_files]): + yield row['query'] + + +def trawl_google_trends(terms_files, outfile = None, mode='w'): + terms = list(read_google_trends_files(terms_files)) + resultset = run_wikidata_searches(terms) + resultset.to_csv(outfile, mode) + +def trawl_base_terms(infiles, outfile = None, mode='w'): + terms = list(chain(* (open(infile,'r') for infile in infiles))) + resultset = run_wikidata_searches(terms) + resultset.to_csv(outfile, mode) + + ## search each of the base terms in wikidata + + # store unique entities found in the search results, the position in the search result, and the date + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser("Search wikidata for entities related to a set of terms.") + parser.add_argument('inputs', type=str, nargs='+', help='one or more files to read') + parser.add_argument('--use-gtrends', action='store_true', help = 'toggle whether the input is the output from google trends') + parser.add_argument('--output', type=str, help='an output file. defaults to stdout') + parser.add_argument('--overwrite', action='store_true', help = 'overwrite existing output files instead of appending') + args = parser.parse_args() + if args.use_gtrends: + trawl_google_trends(args.inputs, args.output) + else: + trawl_base_terms(args.inputs, args.output) diff --git a/transliterations/src/wikidata_transliterations.py b/transliterations/src/wikidata_transliterations.py new file mode 100644 index 0000000..0856c9c --- /dev/null +++ b/transliterations/src/wikidata_transliterations.py @@ -0,0 +1,96 @@ +from wikidata_api_calls import run_sparql_query +from itertools import chain, islice +import csv +from json import JSONDecodeError + +class LabelData: + __slots__ = ['entityid','label','langcode','is_alt'] + + def __init__(self, wd_res, is_alt): + obj = wd_res.get('label',None) + self.label = obj.get('value',None) + self.langcode = obj.get('xml:lang',None) + self.entityid = wd_res.get('entity',None).get('value',None) + self.is_alt = is_alt + + def to_list(self): + return [self.entityid, + self.label, + self.langcode, + self.is_alt] + +def GetAllLabels(in_csvs, outfile, topNs): + + def load_entity_ids(in_csv, topN=5): + with open(in_csv,'r',newline='') as infile: + reader = list(csv.DictReader(infile)) + for row in reader: + if int(row['search_position']) < topN: + yield row["entityid"] + + ids = set(chain(* map(lambda in_csv, topN: load_entity_ids(in_csv, topN), in_csvs, topNs))) + + labeldata = GetEntityLabels(ids) + + with open(outfile, 'w', newline='') as of: + writer = csv.writer(of) + writer.writerow(LabelData.__slots__) + writer.writerows(map(LabelData.to_list,labeldata)) + + +def GetEntityLabels(entityids): + + def run_query_and_parse(query, is_alt): + results = run_sparql_query(query) + try: + jobj = results.json() + + res = jobj.get('results',None) + if res is not None: + res = res.get('bindings',None) + if res is None: + raise requests.APIError(f"got invalid response from wikidata for {query % entityid}") + + for info in res: + yield LabelData(info, is_alt) + + except JSONDecodeError as e: + print(e) + print(query) + + def prep_query(query, prop, entityids): + values = ' '.join(('wd:{0}'.format(id) for id in entityids)) + return query.format(prop, values) + + base_query = """ + SELECT DISTINCT ?entity ?label WHERE {{ + ?entity {0} ?label; + VALUES ?entity {{ {1} }} + }}""" + + # we can't get all the entities at once. how about 100 at a time? + chunksize = 100 + entityids = (id for id in entityids) + chunk = list(islice(entityids, chunksize)) + calls = [] + while len(chunk) > 0: + label_query = prep_query(base_query, "rdfs:label", chunk) + altLabel_query = prep_query(base_query, "skos:altLabel", chunk) + label_results = run_query_and_parse(label_query, is_alt=False) + altLabel_results = run_query_and_parse(altLabel_query, is_alt=True) + calls.extend([label_results, altLabel_results]) + chunk = list(islice(entityids, chunksize)) + + return chain(*calls) + + +if __name__ == "__main__": + import argparse + parser = argparse.ArgumentParser("Use wikidata to find transliterations of terms") + parser.add_argument('inputs', type=str, nargs='+', help='one or more files to read. the inputs are generated by wikidata_search.py') + parser.add_argument('--topN', type=int, nargs='+', help='limit number of wikidata search results to use, can pass one arg for each source.') + parser.add_argument('--output', type=str, help='an output file. defaults to stdout',default=20) + + args = parser.parse_args() + + GetAllLabels(args.inputs, args.output, topNs=args.topN)