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import requests
import feedparser
import datetime
def fetch_arxiv_bias_fairness(days=3):
"""
从 arXiv 中搜索过去 N 天内包含 'bias' OR 'fairness' 等关键词的论文
分类限定为 cs.IR (可自行改)，使用 all: 字段 + submittedDate range + 本地过滤
"""
now = datetime.datetime.utcnow()
start_day = now - datetime.timedelta(days=days)
# 构造日期范围 (只精确到天就行)
# 格式: [YYYYMMDD0000 TO YYYYMMDD2359]
start_str = start_day.strftime("%Y%m%d0000")
end_str = now.strftime("%Y%m%d2359")
# arXiv 布尔搜索表达式
# 这里演示2个关键词 bias, fairness
# 用 (all:bias OR all:fairness)
# 同时限制分类 cat:cs.IR
# 同时限制日期 submittedDate:[start_str TO end_str]
# 并指定 sortBy=submittedDate
search_query = f"(all:bias+OR+all:fairness)+AND+cat:cs.IR+AND+submittedDate:[{start_str}+TO+{end_str}]"
base_url = "http://export.arxiv.org/api/query"
params = {
"search_query": search_query,
"sortBy": "submittedDate",
"sortOrder": "descending",
"max_results": 100
}
print("[DEBUG] search_query=", search_query)
response = requests.get(base_url, params=params)
print("[DEBUG] Full URL =", response.url)
if response.status_code != 200:
print("[ERROR] HTTP Status:", response.status_code)
return []
feed = feedparser.parse(response.content)
entries = feed.entries
print("[DEBUG] arXiv 返回条数:", len(entries))
papers = []
for e in entries:
title = e.title
summary = e.summary
published = e.published
link = e.link
# 在本地再做一个严格的匹配
# 看标题或摘要中是否真的含 bias/fairness
# 以免 all:bias 命中其他字段
text = (title + " " + summary).lower()
if ("bias" in text) or ("fairness" in text):
papers.append({
"title": title,
"published": published,
"link": link
})
return papers
if __name__ == "__main__":
# 测试过去3天
results = fetch_arxiv_bias_fairness(days=3)
print(f"找到 {len(results)} 篇论文：")
for i, p in enumerate(results, 1):
print(f"{i}. {p['title']} - {p['published']} - {p['link']}")
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