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WikiwhoRelationships.py
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WikiwhoRelationships.py
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'''
Created on Feb 20, 2013
@author: Maribel Acosta
@author: Fabian Floeck
'''
from wmf import dump
from difflib import Differ
from time import time
from structures.Revision import Revision
from structures.Paragraph import Paragraph
from structures.Sentence import Sentence
from structures.Word import Word
from structures import Text
from etc.Relation import Relation
from sys import argv,exit
import getopt
from copy import deepcopy, copy
# Container of revisions.
revisions = {}
revision_order = []
# Hash tables.
paragraphs_ht = {}
sentences_ht = {}
spam = []
# SPAM detection variables.
CHANGE_PERCENTAGE = -0.40
PREVIOUS_LENGTH = 1000
CURR_LENGTH = 1000
FLAG = "move"
UNMATCHED_PARAGRAPH = 0.0
WORD_DENSITY = 10
WORD_LEN = 100
# Word global id.
WORD_ID = 0
def analyseArticle(file_name):
# Container of relationships.
relations = {}
# Revisions to compare.
revision_curr = Revision()
revision_prev = Revision()
text_curr = None
# Access the file.
dumpIterator = dump.Iterator(file_name)
# Iterate over the pages.
for page in dumpIterator.readPages():
i = 0
# Iterate over revisions of the article.
for revision in page.readRevisions():
vandalism = False
#print "processing rev", revision.getId()
# Update the information about the previous revision.
revision_prev = revision_curr
if (revision.getSha1() == None):
revision.setSha1(Text.calculateHash(revision.getText().encode("utf-8")))
if (revision.getSha1() in spam):
vandalism = True
#TODO: SPAM detection: DELETION
if (revision.getComment()!= None and revision.getComment().find(FLAG) > 0):
pass
else:
if (revision_prev.length > PREVIOUS_LENGTH) and (len(revision.getText()) < CURR_LENGTH) and (((len(revision.getText())-revision_prev.length)/float(revision_prev.length)) <= CHANGE_PERCENTAGE):
vandalism = True
revision_curr = revision_prev
#if (vandalism):
#print "---------------------------- FLAG 1"
#print "SPAM", revision.getId()
#print revision.getText()
#print
if (not vandalism):
# Information about the current revision.
revision_curr = Revision()
revision_curr.id = i
revision_curr.wikipedia_id = int(revision.getId())
revision_curr.length = len(revision.getText())
revision_curr.timestamp = revision.getTimestamp()
revision_curr.comment = revision.getComment()
# Relation of the current relation.
relation = Relation()
relation.revision = int(revision.getId())
relation.length = len(revision.getText())
# Some revisions don't have contributor.
if (revision.getContributor() != None):
revision_curr.contributor_id = revision.getContributor().getId()
revision_curr.contributor_name = revision.getContributor().getUsername().encode('utf-8')
relation.author = revision.getContributor().getUsername().encode('utf-8')
else:
revision_curr.contributor_id = 'Not Available ' + revision.getId()
revision_curr.contribur_name = 'Not Available ' + revision.getId()
relation.author = 'Not Available ' + revision.getId()
# Content within the revision.
text_curr = revision.getText().encode('utf-8')
text_curr = text_curr.lower()
revision_curr.content = text_curr
# Perform comparison.
vandalism = determineAuthorship(revision_curr, revision_prev, text_curr, relation)
if (not vandalism):
#print "NOT SPAM", revision.getId()
# Add the current revision with all the information.
revisions.update({revision_curr.wikipedia_id : revision_curr})
relations.update({revision_curr.wikipedia_id : relation})
revision_order.append((revision_curr.wikipedia_id, False))
# Update the fake revision id.
i = i+1
# Calculate the number of tokens in the revision.
total = 0
for p in revision_curr.ordered_paragraphs:
for paragraph_curr in revision_curr.paragraphs[p]:
for hash_sentence_curr in paragraph_curr.sentences.keys():
for sentence_curr in paragraph_curr.sentences[hash_sentence_curr]:
total = total + len(sentence_curr.words)
revision_curr.total_tokens = total
relation.total_tokens = total
else:
#print "---------------------------- FLAG 2"
#print "SPAM", revision.getId()
#print revision.getText()
#print
revision_order.append((revision_curr.wikipedia_id, True))
revision_curr = revision_prev
spam.append(revision.getSha1())
return (revisions, revision_order, relations)
def determineAuthorship(revision_curr, revision_prev, text_curr, relation):
# Containers for unmatched paragraphs and sentences in both revisions.
unmatched_sentences_curr = []
unmatched_sentences_prev = []
matched_sentences_prev = []
matched_words_prev = []
possible_vandalism = False
vandalism = False
# Analysis of the paragraphs in the current revision.
(unmatched_paragraphs_curr, unmatched_paragraphs_prev, matched_paragraphs_prev) = analyseParagraphsInRevision(revision_curr, revision_prev, text_curr, relation)
# Analysis of the sentences in the unmatched paragraphs of the current revision.
if (len(unmatched_paragraphs_curr)>0):
(unmatched_sentences_curr, unmatched_sentences_prev, matched_sentences_prev, _) = analyseSentencesInParagraphs(unmatched_paragraphs_curr, unmatched_paragraphs_prev, revision_curr, revision_prev, relation)
#TODO: SPAM detection
if (len(unmatched_paragraphs_curr)/float(len(revision_curr.ordered_paragraphs)) > UNMATCHED_PARAGRAPH):
possible_vandalism = True
# Analysis of words in unmatched sentences (diff of both texts).
if (len(unmatched_sentences_curr)>0):
(matched_words_prev, vandalism) = analyseWordsInSentences(unmatched_sentences_curr, unmatched_sentences_prev, revision_curr, possible_vandalism, relation)
if (len(unmatched_paragraphs_curr) == 0):
for paragraph in unmatched_paragraphs_prev:
for sentence_key in paragraph.sentences.keys():
for sentence in paragraph.sentences[sentence_key]:
if not(sentence.matched):
unmatched_sentences_prev.append(sentence)
# Add the information of 'deletion' to words
for unmatched_sentence in unmatched_sentences_prev:
#if (revision_curr.wikipedia_id == 74544182):
#print "unmatched sentence", unmatched_sentence.value, revision_curr.wikipedia_id
for word_prev in unmatched_sentence.words:
if not(word_prev.matched):
for elem in word_prev.deleted:
if (elem != revision_curr.wikipedia_id) and (elem in revisions.keys()):
if (revisions[elem].contributor_name != revision_curr.contributor_name):
if (elem in relation.redeleted.keys()):
relation.redeleted.update({elem : relation.redeleted[elem] + 1})
else:
relation.redeleted.update({elem : 1})
else:
if (elem in relation.self_redeleted.keys()):
relation.self_redeleted.update({elem : relation.self_redeleted[elem] + 1})
else:
relation.self_redeleted.update({elem : 1})
# Revert: deleting something that somebody else reintroduced.
for elem in word_prev.freq:
#if (revision_curr.wikipedia_id == 11):
# print "Revert in 11", word_prev.value, word_prev.deleted, relation.revert
if (elem != revision_curr.wikipedia_id) and (elem in revisions.keys()):
if (revisions[elem].contributor_name != revision_curr.contributor_name):
if (elem in relation.revert.keys()):
relation.revert.update({elem: relation.revert[elem] +1})
else:
relation.revert.update({elem: 1})
else:
if (elem in relation.self_revert.keys()):
relation.self_revert.update({elem: relation.self_revert[elem] +1})
else:
relation.self_revert.update({elem: 1})
#print "relation.revert", word_prev.value, word_prev.deleted, relation.revert, revision_curr.wikipedia_id
word_prev.deleted.append(revision_curr.wikipedia_id)
#if (revision_curr.wikipedia_id == 74544182):
# print word_prev.value, word_prev.revision, revisions[word_prev.revision].contributor_name, revision_curr.contributor_name
if (revisions[word_prev.revision].contributor_name != revision_curr.contributor_name):
if (word_prev.revision in relation.deleted.keys()):
relation.deleted.update({word_prev.revision : relation.deleted[word_prev.revision] + 1 })
else:
relation.deleted.update({word_prev.revision : 1 })
else:
if (word_prev.revision in relation.self_deleted.keys()):
relation.self_deleted.update({word_prev.revision : relation.self_deleted[word_prev.revision] + 1 })
else:
relation.self_deleted.update({word_prev.revision : 1 })
# Reset matched structures from old revisions.
for matched_paragraph in matched_paragraphs_prev:
matched_paragraph.matched = False
for sentence_hash in matched_paragraph.sentences.keys():
for sentence in matched_paragraph.sentences[sentence_hash]:
sentence.matched = False
for word in sentence.words:
word.matched = False
for matched_sentence in matched_sentences_prev:
matched_sentence.matched = False
for word in matched_sentence.words:
word.matched = False
for matched_word in matched_words_prev:
matched_word.matched = False
if (not vandalism):
# Add the new paragraphs to hash table of paragraphs.
for unmatched_paragraph in unmatched_paragraphs_curr:
if (unmatched_paragraph.hash_value in paragraphs_ht.keys()):
paragraphs_ht[unmatched_paragraph.hash_value].append(unmatched_paragraph)
else:
paragraphs_ht.update({unmatched_paragraph.hash_value : [unmatched_paragraph]})
# Add the new sentences to hash table of sentences.
for unmatched_sentence in unmatched_sentences_curr:
if (unmatched_sentence.hash_value in sentences_ht.keys()):
sentences_ht[unmatched_sentence.hash_value].append(unmatched_sentence)
else:
sentences_ht.update({unmatched_sentence.hash_value : [unmatched_sentence]})
return vandalism
def analyseParagraphsInRevision(revision_curr, revision_prev, text_curr, relation):
# Containers for unmatched and matched paragraphs.
unmatched_paragraphs_curr = []
unmatched_paragraphs_prev = []
matched_paragraphs_prev = []
# Split the text of the current into paragraphs.
paragraphs = Text.splitIntoParagraphs(text_curr)
# Iterate over the paragraphs of the current version.
for paragraph in paragraphs:
# Build Paragraph structure and calculate hash value.
paragraph = paragraph.strip()
hash_curr = Text.calculateHash(paragraph)
matched_curr = False
# If the paragraph is in the previous revision,
# update the authorship information and mark both paragraphs as matched (also in HT).
if (hash_curr in revision_prev.ordered_paragraphs):
for paragraph_prev in revision_prev.paragraphs[hash_curr]:
if (not paragraph_prev.matched):
matched_curr = True
paragraph_prev.matched = True
matched_paragraphs_prev.append(paragraph_prev)
# TODO: added this (CHECK).
for hash_sentence_prev in paragraph_prev.sentences.keys():
for sentence_prev in paragraph_prev.sentences[hash_sentence_prev]:
sentence_prev.matched = True
for word_prev in sentence_prev.words:
word_prev.matched = True
word_prev.used.append(revision_curr.wikipedia_id)
#if (word_prev.revision in relation.reintroduced.keys()):
# relation.reintroduced.update({word_prev.revision : relation.reintroduced[word_prev.revision] + 1 })
#else:
# relation.reintroduced.update({word_prev.revision : 1 })
# Add paragraph to current revision.
if (hash_curr in revision_curr.paragraphs.keys()):
revision_curr.paragraphs[paragraph_prev.hash_value].append(paragraph_prev)
revision_curr.ordered_paragraphs.append(paragraph_prev.hash_value)
else:
revision_curr.paragraphs.update({paragraph_prev.hash_value : [paragraph_prev]})
revision_curr.ordered_paragraphs.append(paragraph_prev.hash_value)
break
# If the paragraph is not in the previous revision, but it is in an older revision
# update the authorship information and mark both paragraphs as matched.
if ((not matched_curr) and (hash_curr in paragraphs_ht)):
for paragraph_prev in paragraphs_ht[hash_curr]:
if (not paragraph_prev.matched):
matched_curr = True
paragraph_prev.matched = True
matched_paragraphs_prev.append(paragraph_prev)
# TODO: added this (CHECK).
for hash_sentence_prev in paragraph_prev.sentences.keys():
for sentence_prev in paragraph_prev.sentences[hash_sentence_prev]:
sentence_prev.matched = True
for word_prev in sentence_prev.words:
word_prev.matched = True
word_prev.used.append(revision_curr.wikipedia_id)
if (revision_prev.wikipedia_id not in word_prev.used):
word_prev.freq.append(revision_curr.wikipedia_id)
# Revert: reintroducing something that somebody else deleted,
# (and was not used in the previous revision)
if (revision_prev.wikipedia_id not in word_prev.used):
#if (revision_curr.wikipedia_id == 11):
# print "Revert in 11", word_prev.value, word_prev.deleted, relation.revert
for elem in word_prev.deleted:
if (elem in revisions.keys()):
if (revisions[elem].contributor_name != revision_curr.contributor_name):
if (elem in relation.revert.keys()):
relation.revert.update({elem : relation.revert[elem] + 1})
else:
relation.revert.update({elem : 1})
else:
if (elem in relation.self_revert.keys()):
relation.self_revert.update({elem : relation.self_revert[elem] + 1})
else:
relation.self_revert.update({elem : 1})
if (revision_prev.wikipedia_id not in word_prev.used):
if (elem in revisions.keys()):
if (revisions[word_prev.revision].contributor_name != revision_curr.contributor_name):
if (word_prev.revision in relation.reintroduced.keys()):
relation.reintroduced.update({word_prev.revision : relation.reintroduced[word_prev.revision] + 1 })
else:
relation.reintroduced.update({word_prev.revision : 1 })
else:
if (word_prev.revision in relation.self_reintroduced.keys()):
relation.self_reintroduced.update({word_prev.revision : relation.self_reintroduced[word_prev.revision] + 1})
else:
relation.self_reintroduced.update({word_prev.revision : 1})
# Add paragraph to current revision.
if (hash_curr in revision_curr.paragraphs.keys()):
revision_curr.paragraphs[paragraph_prev.hash_value].append(paragraph_prev)
revision_curr.ordered_paragraphs.append(paragraph_prev.hash_value)
else:
revision_curr.paragraphs.update({paragraph_prev.hash_value : [paragraph_prev]})
revision_curr.ordered_paragraphs.append(paragraph_prev.hash_value)
break
# If the paragraph did not match with previous revisions,
# add to container of unmatched paragraphs for further analysis.
if (not matched_curr):
paragraph_curr = Paragraph()
paragraph_curr.hash_value = Text.calculateHash(paragraph)
paragraph_curr.value = paragraph
revision_curr.ordered_paragraphs.append(paragraph_curr.hash_value)
if (paragraph_curr.hash_value in revision_curr.paragraphs.keys()):
revision_curr.paragraphs[paragraph_curr.hash_value].append(paragraph_curr)
else:
revision_curr.paragraphs.update({paragraph_curr.hash_value : [paragraph_curr]})
unmatched_paragraphs_curr.append(paragraph_curr)
# Identify unmatched paragraphs in previous revision for further analysis.
for paragraph_prev_hash in revision_prev.ordered_paragraphs:
for paragraph_prev in revision_prev.paragraphs[paragraph_prev_hash]:
if (not paragraph_prev.matched):
unmatched_paragraphs_prev.append(paragraph_prev)
return (unmatched_paragraphs_curr, unmatched_paragraphs_prev, matched_paragraphs_prev)
def analyseSentencesInParagraphs(unmatched_paragraphs_curr, unmatched_paragraphs_prev, revision_curr, revision_prev, relation):
# Containers for unmatched and matched sentences.
unmatched_sentences_curr = []
unmatched_sentences_prev = []
matched_sentences_prev = []
total_sentences = 0
# Iterate over the unmatched paragraphs of the current revision.
for paragraph_curr in unmatched_paragraphs_curr:
# Split the current paragraph into sentences.
sentences = Text.splitIntoSentences(paragraph_curr.value)
# Iterate over the sentences of the current paragraph
for sentence in sentences:
# Create the Sentence structure.
sentence = sentence.strip()
sentence = ' '.join(Text.splitIntoWords(sentence))
hash_curr = Text.calculateHash(sentence)
matched_curr = False
total_sentences = total_sentences + 1
# Iterate over the unmatched paragraphs from the previous revision.
for paragraph_prev in unmatched_paragraphs_prev:
if (hash_curr in paragraph_prev.sentences.keys()):
for sentence_prev in paragraph_prev.sentences[hash_curr]:
if (not sentence_prev.matched):
matched_one = False
matched_all = True
for word_prev in sentence_prev.words:
if (word_prev.matched):
matched_one = True
else:
matched_all = False
if not(matched_one):
sentence_prev.matched = True
matched_curr = True
matched_sentences_prev.append(sentence_prev)
# TODO: CHECK this
for word_prev in sentence_prev.words:
word_prev.matched = True
word_prev.used.append(revision_curr.wikipedia_id)
#if (word_prev.revision in relation.reintroduced.keys()):
# relation.reintroduced.update({word_prev.revision : relation.reintroduced[word_prev.revision] + 1 })
#else:
# relation.reintroduced.update({word_prev.revision : 1 })
# Add the sentence information to the paragraph.
if (hash_curr in paragraph_curr.sentences.keys()):
paragraph_curr.sentences[hash_curr].append(sentence_prev)
paragraph_curr.ordered_sentences.append(sentence_prev.hash_value)
else:
paragraph_curr.sentences.update({sentence_prev.hash_value : [sentence_prev]})
paragraph_curr.ordered_sentences.append(sentence_prev.hash_value)
break
elif (matched_all):
sentence_prev.matched = True
matched_sentences_prev.append(sentence_prev)
if (matched_curr):
break
# Iterate over the hash table of sentences from old revisions.
if ((not matched_curr) and (hash_curr in sentences_ht.keys())):
for sentence_prev in sentences_ht[hash_curr]:
if (not sentence_prev.matched):
matched_one = False
matched_all = True
for word_prev in sentence_prev.words:
if (word_prev.matched):
matched_one = True
else:
matched_all = False
if not(matched_one):
sentence_prev.matched = True
matched_curr = True
matched_sentences_prev.append(sentence_prev)
# TODO: CHECK this
for word_prev in sentence_prev.words:
word_prev.matched = True
word_prev.used.append(revision_curr.wikipedia_id)
if (revision_prev.wikipedia_id not in word_prev.used):
word_prev.freq.append(revision_curr.wikipedia_id)
# Revert: reintroducing something that somebody else deleted
if (revision_prev.wikipedia_id not in word_prev.used):
for elem in word_prev.deleted:
#if (revision_curr.wikipedia_id == 11):
# print "Revert in 11", word_prev.value, word_prev.deleted, relation.revert
if (elem in revisions.keys()):
if (revisions[elem].contributor_name != revision_curr.contributor_name):
if (elem in relation.revert.keys()):
relation.revert.update({elem : relation.revert[elem] + 1})
else:
relation.revert.update({elem : 1})
else:
if (elem in relation.self_revert.keys()):
relation.self_revert.update({elem : relation.self_revert[elem] + 1})
else:
relation.self_revert.update({elem : 1})
#print "relation.revert", word_prev.value, word_prev.deleted, relation.revert, revision_curr.wikipedia_id
if (revision_prev.wikipedia_id not in word_prev.used):
if (elem in revisions.keys()):
if (revisions[word_prev.revision].contributor_name != revision_curr.contributor_name):
if (word_prev.revision in relation.reintroduced.keys()):
relation.reintroduced.update({word_prev.revision : relation.reintroduced[word_prev.revision] + 1 })
else:
relation.reintroduced.update({word_prev.revision : 1 })
else:
if (word_prev.revision in relation.self_reintroduced.keys()):
relation.self_reintroduced.update({word_prev.revision : relation.self_reintroduced[word_prev.revision] + 1})
else:
relation.self_reintroduced.update({word_prev.revision : 1})
# Add the sentence information to the paragraph.
if (hash_curr in paragraph_curr.sentences.keys()):
paragraph_curr.sentences[hash_curr].append(sentence_prev)
paragraph_curr.ordered_sentences.append(sentence_prev.hash_value)
else:
paragraph_curr.sentences.update({sentence_prev.hash_value : [sentence_prev]})
paragraph_curr.ordered_sentences.append(sentence_prev.hash_value)
break
elif (matched_all):
sentence_prev.matched = True
matched_sentences_prev.append(sentence_prev)
# If the sentence did not match, then include in the container of unmatched sentences for further analysis.
if (not matched_curr):
sentence_curr = Sentence()
sentence_curr.value = sentence
sentence_curr.hash_value = hash_curr
paragraph_curr.ordered_sentences.append(sentence_curr.hash_value)
if (sentence_curr.hash_value in paragraph_curr.sentences.keys()):
paragraph_curr.sentences[sentence_curr.hash_value].append(sentence_curr)
else:
paragraph_curr.sentences.update({sentence_curr.hash_value : [sentence_curr]})
unmatched_sentences_curr.append(sentence_curr)
# Identify the unmatched sentences in the previous paragraph revision.
for paragraph_prev in unmatched_paragraphs_prev:
for sentence_prev_hash in paragraph_prev.ordered_sentences:
for sentence_prev in paragraph_prev.sentences[sentence_prev_hash]:
if (not sentence_prev.matched):
unmatched_sentences_prev.append(sentence_prev)
sentence_prev.matched = True
matched_sentences_prev.append(sentence_prev)
return (unmatched_sentences_curr, unmatched_sentences_prev, matched_sentences_prev, total_sentences)
def analyseWordsInSentences(unmatched_sentences_curr, unmatched_sentences_prev, revision_curr, possible_vandalism, relation):
matched_words_prev = []
unmatched_words_prev = []
global WORD_ID
# Split sentences into words.
text_prev = []
for sentence_prev in unmatched_sentences_prev:
for word_prev in sentence_prev.words:
if (not word_prev.matched):
text_prev.append(word_prev.value)
unmatched_words_prev.append(word_prev)
text_curr = []
for sentence_curr in unmatched_sentences_curr:
splitted = Text.splitIntoWords(sentence_curr.value)
text_curr.extend(splitted)
sentence_curr.splitted.extend(splitted)
# Edit consists of removing sentences, not adding new content.
if (len(text_curr) == 0):
return (matched_words_prev, False)
# SPAM detection.
if (possible_vandalism):
density = Text.computeAvgWordFreq(text_curr, revision_curr.wikipedia_id)
if (density > WORD_DENSITY):
return (matched_words_prev, possible_vandalism)
else:
possible_vandalism = False
if (len(text_prev) == 0):
for sentence_curr in unmatched_sentences_curr:
for word in sentence_curr.splitted:
word_curr = Word()
word_curr.internal_id = WORD_ID
word_curr.author_id = revision_curr.contributor_id
word_curr.author_name = revision_curr.contributor_name
word_curr.revision = revision_curr.wikipedia_id
word_curr.value = word
sentence_curr.words.append(word_curr)
word_curr.used.append(revision_curr.wikipedia_id)
relation.added = relation.added + 1
WORD_ID = WORD_ID + 1
return (matched_words_prev, possible_vandalism)
d = Differ()
diff = list(d.compare(text_prev, text_curr))
for sentence_curr in unmatched_sentences_curr:
for word in sentence_curr.splitted:
curr_matched = False
pos = 0
while (pos < len(diff)):
word_diff = diff[pos]
if (word == word_diff[2:]):
if (word_diff[0] == ' '):
for word_prev in unmatched_words_prev:
if ((not word_prev.matched) and (word_prev.value == word)):
word_prev.used.append(revision_curr.wikipedia_id)
word_prev.matched = True
curr_matched = True
sentence_curr.words.append(word_prev)
matched_words_prev.append(word_prev)
diff[pos] = ''
pos = len(diff)+1
#if (word_prev.revision in relation.reintroduced.keys()):
# relation.reintroduced.update({word_prev.revision : relation.reintroduced[word_prev.revision] + 1 })
#else:
# relation.reintroduced.update({word_prev.revision : 1 })
break
elif (word_diff[0] == '-'):
for word_prev in unmatched_words_prev:
if ((not word_prev.matched) and (word_prev.value == word)):
word_prev.matched = True
matched_words_prev.append(word_prev)
diff[pos] = ''
word_prev.deleted.append(revision_curr.wikipedia_id)
if (revisions[word_prev.revision].contributor_name != revision_curr.contributor_name):
if (word_prev.revision in relation.deleted.keys()):
relation.deleted.update({word_prev.revision : relation.deleted[word_prev.revision] + 1 })
else:
relation.deleted.update({word_prev.revision : 1 })
else:
if (word_prev.revision in relation.self_deleted.keys()):
relation.self_deleted.update({word_prev.revision : relation.self_deleted[word_prev.revision] + 1 })
else:
relation.self_deleted.update({word_prev.revision : 1 })
break
elif (word_diff[0] == '+'):
curr_matched = True
word_curr = Word()
word_curr.internal_id = WORD_ID
word_curr.value = word
word_curr.author_id = revision_curr.contributor_id
word_curr.author_name = revision_curr.contributor_name
word_curr.revision = revision_curr.wikipedia_id
word_curr.used.append(revision_curr.wikipedia_id)
sentence_curr.words.append(word_curr)
relation.added = relation.added + 1
WORD_ID = WORD_ID + 1
diff[pos] = ''
pos = len(diff)+1
pos = pos + 1
if not(curr_matched):
word_curr = Word()
word_curr.internal_id = WORD_ID
word_curr.value = word
word_curr.author_id = revision_curr.contributor_id
word_curr.author_name = revision_curr.contributor_name
word_curr.revision = revision_curr.wikipedia_id
word_curr.used.append(revision_curr.wikipedia_id)
sentence_curr.words.append(word_curr)
relation.added = relation.added + 1
WORD_ID = WORD_ID + 1
return (matched_words_prev, possible_vandalism)
def printAllRevisions(order, revisions):
for (revision, vandalism) in order:
if not(vandalism):
printRevision(revisions[revision])
def printRevision(revision):
print "Printing authorhship for revision: ", revision.wikipedia_id
text = []
authors = []
for hash_paragraph in revision.ordered_paragraphs:
#print hash_paragraph
#text = ''
p_copy = deepcopy(revision.paragraphs[hash_paragraph])
paragraph = p_copy.pop(0)
#print paragraph.value
#print len(paragraph.sentences)
for hash_sentence in paragraph.ordered_sentences:
#print hash_sentence
sentence = paragraph.sentences[hash_sentence].pop(0)
#print sentence.words
for word in sentence.words:
#print word
#text = text + ' ' + unicode(word.value,'utf-8') + "@@" + str(word.revision)
text.append(word.value)
authors.append(word.revision)
print text
print authors
def printRevisionTrackAppearance(revision):
print "Printing authorship for revision: ", revision.wikipedia_id
text = []
authors = []
for hash_paragraph in revision.ordered_paragraphs:
#print hash_paragraph
#text = ''
p_copy = deepcopy(revision.paragraphs[hash_paragraph])
paragraph = p_copy.pop(0)
#print paragraph.value
#print len(paragraph.sentences)
for hash_sentence in paragraph.ordered_sentences:
#print hash_sentence
sentence = paragraph.sentences[hash_sentence].pop(0)
#print sentence.words
for word in sentence.words:
appeared = copy(word.used)
disappeared = copy(word.deleted)
changes = []
changes.append("+(" + str(appeared.pop(0))+")")
while len(disappeared) > 0:
d = disappeared.pop(0)
if (d > revision.wikipedia_id):
break
changes.append("-(" + str(d)+")")
while len(appeared) > 0:
a = appeared.pop(0)
if (a > d):
changes.append("+(" + str(a)+")")
break
#print word.used
#print word.deleted
print unicode(word.value,'utf-8') + "@@" + str(word.revision) + "@@" + str(changes)
text.append(word.value)
authors.append(word.revision)
#print text
#print authors
def printRelationships(relations, order):
print "Printing relationships"
header = ["revision", "author", "deleted(-)", "revert(-)", "reintroduced(+)", "redeleted(+)", "added", "total", "self-deleted", "self-revert", "self-reintroduced", "self-redeleted"]
print "\t".join(header)
for (revision, vandalism) in order:
if (vandalism):
continue
relation = relations[revision]
#print relation.author
print str(relation.revision) + "\t" + (relation.author).decode("utf-8") + "\t" + str(relation.deleted) + "\t" + str(relation.revert) + "\t" + str(relation.reintroduced) + "\t" + str(relation.redeleted) + "\t" + str(relation.added) + "\t" + str(relation.total_tokens) + "\t" + str(relation.self_deleted) + "\t" + str(relation.self_revert) + "\t" + str(relation.self_reintroduced) + "\t" + str(relation.self_redeleted)
#def printJSON(relations, order):
#
# deleted_values = {}
# revert_values = {}
# reintroduced_values = {}
# redeleted_values = {}
#
# for (revision, vandalism) in order:
# if (vandalism):
# continue
# relation = relations[revision]
# print str(relation.revision) + "\t" + str(relation.author) + "\t" + str(relation.deleted) + "\t" + str(relation.revert) + "\t" + str(relation.reintroduced) + "\t" + str(relation.redeleted) + "\t" + str(relation.added) + "\t" + str(relation.total_tokens)
def printRevisionJSON(revision):
tokens = []
for hash_paragraph in revision.ordered_paragraphs:
p_copy = deepcopy(revision.paragraphs[hash_paragraph])
paragraph = p_copy.pop(0)
for hash_sentence in paragraph.ordered_sentences:
sentence = paragraph.sentences[hash_sentence].pop(0)
for word in sentence.words:
i = 0
while i < len(word.deleted):
if word.deleted[i] > revision.wikipedia_id:
break
i = i+1
j = 0
while j < len(word.freq):
if word.freq[j] > revision.wikipedia_id:
break
j = j +1
tokens.append(str({"token": word.value, "author_name": word.author_name.encode("utf-8"), "rev_id": str(word.revision), "disappering": word.deleted[0:i], "incoming": word.freq[0:j]}))
print tokens #cjson.encode({"tokens" : tokens})
def main(my_argv):
inputfile = ''
revision = None
output = ''
if (len(my_argv) <= 3):
try:
opts, _ = getopt.getopt(my_argv,"i:",["ifile="])
except getopt.GetoptError:
print 'Usage: Wikiwho.py -i <inputfile> -o <output> [-rev <revision_id>]'
exit(2)
else:
try:
opts, _ = getopt.getopt(my_argv,"i:o:r:",["ifile=","revision=", "output="])
except getopt.GetoptError:
print 'Usage: Wikiwho.py -i <inputfile> -o output [-rev <revision_id>]'
exit(2)
for opt, arg in opts:
if opt in ('-h', "--help"):
print "WikiWho: An algorithm for detecting attribution of authorship in revisioned content"
print
print 'Usage: Wikiwho.py -i <inputfile> [-rev <revision_id>]'
print "-i --ifile File to analyze"
print "-o --type of output: <a> for authorship, <r> for relations"
print "-r --revision Revision to analyse. If not specified, the last revision is printed."
print "-h --help This help."
exit()
elif opt in ("-i", "--ifile"):
inputfile = arg
elif opt in ("-r", "--revision"):
revision = arg
elif opt in ("-o", "--output"):
output = arg
return (inputfile,revision,output)
if __name__ == '__main__':
(file_name, revision, output) = main(argv[1:])
#print "Calculating authorship for:", file_name
time1 = time()
(revisions, order, relations) = analyseArticle(file_name)
time2 = time()
#pos = file_name.rfind("/")
#print file_name[pos+1: len(file_name)-len(".xml")], time2-time1
if (output == 'r'):
printRelationships(relations, order)
if (output == 'a'):
print "revision", revision
if (revision == 'all'):
printAllRevisions(order, revisions)
else:
printRevisionJSON(revisions[int(revision)])
#print "Execution time:", time2-time1