Instagram isn’t just about the present, it is also about nostalgia for the recent past – as the very popular hashtags like #Throwback or #TbT reveal. A recent trend has been the #TakeMeBack hashtag, which accompanies the pictures of places people want to return to!
Past trips, holidays, even moments with the family: when routine bites hard, you claw back a fragment of those cherished times and memories by editing or re-posting a forgotten holiday snap.
However, not all holiday memories are the same: some destinations happen to create warmer memories than others. While frequent travellers remained locked down during the several last past weeks, researchers have been analysing thousands of Instagram posts.
Researchers have pinpointed the sites, attractions, and landmarks that are missed the most. This was achieved by extracting location data for 208,362 Instagram posts with the #TakeMeBack hashtag and organising them by location.
When Instagram cries “take me back,” this is where it wants to go:
Top 10 #TakeMeBack Cities in the UK
- London – England
- Edinburgh – Scotland
- Manchester – North West, England
- Liverpool – North West, England
- Bristol – South West, England
- Watford – East of England
- Brighton – South East, England
- Birmingham – West Midlands, England
- Leeds – Yorkshire and the Humber, England
- Oxford – South East, England
The Top 5 #TakeMeBack Places in the UK
- Tower Bridge (London)
- London Eye (London)
- Buckingham Palace (London)
- Notting Hill (London)
- Warner Bros. Studio Tour (Watford)
Researchers extracted location data for 208,362 Instagram posts tagged with the #TakeMeBack hashtag. They then cleaned up the data and organised it by location to help create the interactive you see today. The data gathering was done in April 2020.
Notes: Researchers used Google Places API to normalise places names. For example, the Instagram tag name can be ‘Eiffel Tower’ or ‘Tour Eiffel’ or ‘La Tour Eiffel’ or ‘eiffel tower’ or ‘エッフェル塔’. Merge all these items manually would have taken hundreds of hours, so researchers used this Google Places API to normalize all names.
You can find the research original source HERE.