def _get_item(self, soup):
li_item_list = soup.findAll('li')
for li_item in li_item_list:
if li_item.find('div'):
if li_item.find('div').find('h4'):
href = li_item.find('div').find('h4').find('a').get('href')
news = News(
str(li_item.find('div').find('h4').find('a').string),
self.dns + href.replace('../', ''), '')
self.news_list.append(news)
def _scrap():
LogUtils.start('Exame')
hrefs, titles = get_news_ignoring_fetched_links(db.get_fetched_links())
news = []
for i in range(len(hrefs)):
href = hrefs[i]
title = titles[i]
paragraphs, publish_date = get_news_content_by_href(href)
news.append(News(title, href, paragraphs, 'EXAME NotÃcias',
'https://abrilexame.files.wordpress.com/2019/08/logo-exame.png?w=150', publish_date))
db.save_all_news(news)
LogUtils.end('Exame', hrefs)
def _scrap():
LogUtils.start('Suno')
news = []
hrefs, titles = get_news_ignoring_fetched_links(db.get_fetched_links())
for i in range(len(hrefs)):
href = hrefs[i]
title = titles[i]
paragraphs, publish_date = get_news_content_by_href(href)
news.append(
News(
title, href, paragraphs, 'Suno NotÃcias',
'https://www.sunoresearch.com.br/wp-content/uploads/2019/12/suno-research.jpg',
publish_date))
db.save_all_news(news)
LogUtils.end('Suno', hrefs)
def _scrap():
LogUtils.start('Info Money')
news = []
hrefs, titles = get_news_ignoring_fetched_links(db.get_fetched_links())
for i in range(len(hrefs)):
href = hrefs[i]
title = titles[i]
paragraphs, publish_date = get_news_content_by_href(href)
news.append(
News(
title, href, paragraphs, 'InfoMoney',
'https://is2-ssl.mzstatic.com/image/thumb/Purple123/v4/5c/df/a9/5cdfa9b4-913f-8b4d-a99d-1c6f2662061e/AppIcon-0-1x_U007emarketing-0-0-85-220-0-4.png/1200x630wa.png',
publish_date))
db.save_all_news(news)
LogUtils.end('Info Money', hrefs)
def run_updater():
stock_dict = json.load(open('resources/stock_links.json'))
stock_list = list(json.load(open('resources/stock_links.json')))
for stock in stock_dict.keys():
try:
news = News.get_by_stock_code(collection="news_articles", stock_code=stock)
if news is None:
new_news = News(stock_code=stock)
new_news.load_articles(query=stock_dict[stock]['name'])
new_news.update_articles()
else:
news.load_articles(stock_dict[stock]['name'])
news.update_articles()
print("Updated: " + stock)
except ConnectionFailure:
print("Unable to connect Mongodb...")
finally:
time.sleep(10)
print("Update completed...")
if d > bdeg:
bdeg = d
best = world.agents[a]
degree_centrality.append(best)
s2 = {}
for agent in degree_centrality:
s2[agent] = news.name
return s2
def activate_agents(agents):
for agent in agents.keys():
agent.states[agents[agent]] = AgentState.ACTIVE
fake_news = News(0, 0.9, 0.5)
counter_news = News(1, 0.5, 0.1)
news_cycle = {fake_news.name: fake_news, counter_news.name: counter_news}
number_agents = 500
names_agents = [_ for _ in range(number_agents)]
threshold = np.clip(np.random.normal(0.5, 0.1, number_agents), 0.0,
1.0) #mu = 0.5, sigma = 0.1
independence = np.full(
number_agents, 0.1
) #np.clip(np.random.normal(0.5, 0.1, number_agents), 0.0, 1.0) #mu = 0.5, sigma = 0.1
w = construct_world(names_agents, threshold, independence, news_cycle)
fakenews_spreader = find_degreecentral_nodes(w, 1, [], fake_news)
counternews_spreader = find_degreecentral_nodes(
w, 5, [a for a in fakenews_spreader.keys()], counter_news)
steps = 30
def create_news(self, json_news):
news_list = []
for news in json_news:
news_list.append(News(news))
return news_list
