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feat: add East Asia news sources and improve geocoding for Taiwan contingency

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Add 5 East Asia-focused RSS feeds (FocusTaiwan, Kyodo, SCMP, The Diplomat,
Stars and Stripes) and 22 geographic keywords (Taiwan Strait, South/East
China Sea, Okinawa, Guam, military bases, etc.) to improve coverage of
Taiwan contingency scenarios.

Refactor keyword matching into a pure _resolve_coords() function with
longest-match-first sorting so specific locations like "Taiwan Strait"
are not absorbed by generic "Taiwan".

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
adust09
2026-03-15 23:19:55 +09:00
co-authored by Claude Opus 4.6
parent 4a33424924
commit 130287bb49
4 changed files with 179 additions and 13 deletions
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backend/services/fetchers/news.py
+47 -13
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@@ -33,6 +33,29 @@ _KEYWORD_COORDS = {
"lebanon": (33.854, 35.862),
"syria": (34.802, 38.996),
"yemen": (15.552, 48.516),
# East Asia — specific locations (longer keywords matched first via _SORTED_KEYWORDS)
"taiwan strait": (24.0, 119.5),
"south china sea": (15.0, 115.0),
"east china sea": (28.0, 125.0),
"philippine sea": (20.0, 130.0),
"senkaku": (25.740, 123.474),
"diaoyu": (25.740, 123.474),
"ryukyu": (26.334, 127.800),
"okinawa": (26.334, 127.800),
"kadena": (26.351, 127.767),
"naha": (26.212, 127.679),
"yokosuka": (35.283, 139.671),
"sasebo": (33.159, 129.722),
"misawa": (40.682, 141.368),
"iwakuni": (34.144, 132.236),
"guam": (13.444, 144.793),
"taipei": (25.033, 121.565),
"kaohsiung": (22.616, 120.313),
"xiamen": (24.479, 118.089),
"fujian": (26.074, 119.296),
"guangdong": (23.379, 113.763),
"zhejiang": (29.141, 119.788),
"hainan": (19.200, 109.999),
"china": (35.861, 104.195),
"beijing": (39.904, 116.407),
"taiwan": (23.697, 120.960),
@@ -90,6 +113,27 @@ _KEYWORD_COORDS = {
"jakarta": (-6.208, 106.845),
}
# Immutable after module load — sort by descending keyword length so
# specific locations ("taiwan strait") match before generic ones ("taiwan")
_SORTED_KEYWORDS = sorted(_KEYWORD_COORDS.items(), key=lambda x: len(x[0]), reverse=True)
def _resolve_coords(text: str) -> tuple[float, float] | None:
"""Return (lat, lng) for the most specific keyword match, or None.
Longer keywords are tried first. Space-padded keywords (" us ", " uk ")
use substring matching on padded text; all others use word-boundary regex.
"""
padded_text = f" {text} "
for kw, coords in _SORTED_KEYWORDS:
if kw.startswith(" ") or kw.endswith(" "):
if kw in padded_text:
return coords
else:
if re.search(r'\b' + re.escape(kw) + r'\b', text):
return coords
return None
@with_retry(max_retries=1, base_delay=2)
def fetch_news():
@@ -140,8 +184,6 @@ def fetch_news():
risk_score += 2
risk_score = min(10, risk_score)
keyword_coords = _KEYWORD_COORDS
lat, lng = None, None
if 'georss_point' in entry:
@@ -153,18 +195,10 @@ def fetch_news():
lat, lng = coords[1], coords[0]
if lat is None:
# text may not be defined yet for GDACS path
text = (title + " " + summary).lower()
padded_text = f" {text} "
for kw, coords in keyword_coords.items():
if kw.startswith(" ") or kw.endswith(" "):
if kw in padded_text:
lat, lng = coords
break
else:
if re.search(r'\b' + re.escape(kw) + r'\b', text):
lat, lng = coords
break
result = _resolve_coords(text)
if result:
lat, lng = result
if lat is not None:
key = None