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ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus
- 1. Peter M. Broadwell @peterbroadwell broadwell@library.ucla.edu Timothy R. Tangherlini @tango63 tango@humnet.ucla.edu ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus
- 2. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Evald Tang Kristensen, Danske Sagn vol. 3, no. 108 Told by Jens Bek and Mikkel Hansen in Lille-Tåning There were a couple of giants that had had a falling out, one in Borum-Eshøj, and the other over in Hasle høj. So they were going to beat each other with maces. The one over on Borum-Eshøj hit first, but his aim was way off and he made that water hole over by Brabrand that they call Brabrand Lake. Then he was going to hit over, but he didn’t have much strength left, and his mace didn’t reach further than Gjeding Lake. So there was no point in him hitting anymore. Now the other one was to go at it, and he started to hit, but was much stronger. He winds up smacking down over Borum-Eshøj, and the spiked ball comes off his mace, and it flies further to the west and makes Lading Lake.
- 3. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Evald Tang Kristensen, Danske Sagn vol. 3, no. 108 Told by Jens Bek and Mikkel Hansen in Lille-Tåning There were a couple of giants that had had a falling out, one in Borum-Eshøj, and the other over in Hasle høj. So they were going to beat each other with maces. The one over on Borum-Eshøj hit first, but his aim was way off and he made that water hole over by Brabrand that they call Brabrand Lake. Then he was going to hit over, but he didn’t have much strength left, and his mace didn’t reach further than Gjeding Lake. So there was no point in him hitting anymore. Now the other one was to go at it, and he started to hit, but was much stronger. He winds up smacking down over Borum-Eshøj, and the spiked ball comes off his mace, and it flies further to the west and makes Lading Lake.
- 4. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Evald Tang Kristensen’s Danish legends
- 5. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 • Collected in Denmark between 1867-1924 • From 3,500 storytellers, mostly in Jutland • 20,431 legends mention a resolvable place name • 6,423 place names -> 2,126 lat/long pairs • 14,254 places mentioned ≠ place told Evald Tang Kristensen’s Danish legends and their geographical context
- 6. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Challenge: geo-topic discovery and exploration • Explore and analyze the relationships between place, meaning and context across arbitrary regions of the geo-located ETK corpus • Use techniques from geo-located social recommendation systems: Where are the elves? What else is in the area? Are there other areas like this one? What factors make them similar?
- 7. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Categories in Evald Tang Kristensen’s Danish legends Witches and their Sport Hidden Folk Witches and their Sport
- 8. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Story categories: multi-level indices Primary categories: Mound dwellers (Hidden folk) Elves Household spirits Traveling monsters Water spirits Wiverns and small creepy-crawlies Werewolves and nightmares Religious legends Death portents Lights and portents Heroes and their sport Churches, monasteries, holy springs Legends about farms and towns Diverse place legends Legends about treasure Small kings and their feuds... Enemy invasions Manor lords, ladies and mistresses Ministers Diverse people Robbers, murderers and thieves Strandings Plague and illnesses Secondary categories: Robber's Christmas eve On grain, rats and mice Hidden folk driving or riding The Devil as a playing companion Jilted lovers bewitched Swedes and Poles north of the Limfjord Destruction of mounds. Animals sick, unrest in the house Sand movements Meadows and swamps Giant graves Cessation of the destruction of a mound Changelings, the old child Bad ministers Giants build churches Giants throw stones at churches The murdered child, mother with the knife and washing it or the clothes Black dogs and the like show themselves Smiths in mounds The church's foundations moved Witches as revenants
- 9. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Geo-semantic exploration Borum-Eshøj Hasle høj Brabrand Gjeding Lake Lading Lake Elves 0 0 0 0 0 Giants and their sport 0 0 0 0 0 Mound dwellers 0 0 0 0 0 There were a couple of giants that had had a falling out, one in Borum- Eshøj, and the other over in Hasle høj. So they were going to beat each other with maces. The one over on Borum-Eshøj hit first, but his aim was way off and he made that water hole over by Brabrand that they call Brabrand Lake. Then he was going to hit over, but he didn’t have much strength left, and his mace didn’t reach further than Gjeding Lake. So there was no point in him hitting anymore. Now the other one was to go at it, and he started to hit, but was much stronger. He winds up smacking down over Borum-Eshøj, and the spiked ball comes off his mace, and it flies further to the west and makes Lading Lake.
- 10. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Geo-semantic exploration Borum-Eshøj Hasle høj Brabrand Gjeding Lake Lading Lake Elves 0 0 0 0 0 Giants and their sport 1 1 1 1 1 Mound dwellers 0 0 0 0 0 There were a couple of giants that had had a falling out, one in Borum- Eshøj, and the other over in Hasle høj. So they were going to beat each other with maces. The one over on Borum-Eshøj hit first, but his aim was way off and he made that water hole over by Brabrand that they call Brabrand Lake. Then he was going to hit over, but he didn’t have much strength left, and his mace didn’t reach further than Gjeding Lake. So there was no point in him hitting anymore. Now the other one was to go at it, and he started to hit, but was much stronger. He winds up smacking down over Borum-Eshøj, and the spiked ball comes off his mace, and it flies further to the west and makes Lading Lake.
- 11. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Geo-semantic exploration Borum-Eshøj Hasle høj Brabrand Gjeding Lake Lading Lake Elves 0 0 0 0 0 Giants and their sport 1 1 1 1 1 Mound dwellers 1 1 0 1 0 A mound man lived in a mound close to Hasle village (the mound has now disappeared and been replaced by a gravel pit), and he was invited once to a birth celebration over at the mound man’s place in Borum- Eshøj, and he was supposed to be the godfather, but the day of the party, his wife got sick and he had to stay home. He didn’t want them not to get his godfather gift, which was going to be a gold hammer, so he went up on his mound to throw it over there. But as he stood there swinging, the hammer head fell off the shaft, and it flew to the northwest and landed in a little dale near Mundelstrup and created Gjeding Lake, but the shaft made it to Borum-Eshøj without leaving a trace.
- 12. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Computational folkloristics and the macroscope Early microscope macroscope
- 13. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Some geo-semantic folklore “scopes” WitchHunter: Visualizes concentrations of story categories in the landscape TrollFinder: For a given area, finds terms and categories that are “characteristic” of stories mentioning places in the area GhostScope: Places all storytellers at the center of the landscape, plots place references to build conceptual maps TreasureX: Links actual places told to places mentioned, plotting the references on a map Börner, Katy. 2011. “Plug-and-Play Macroscopes.” Communications of the ACM 54 (3): 60–69.
- 14. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 WitchHunter: Category/place co-occurrence
- 15. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 TrollFinder: Finding region-specific terms
- 16. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 GhostScope: Storytellers’ conceptual geographies
- 17. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Advanced challenges for ElfYelp • Exploring an unfamiliar region: in an arbitrary location, what salient geo-semantic features of the corpus are found here? • For a given region, how do we find regions that are geo- semantically similar? (Location recommendation problem) • Pairwise comparison of places is computationally expensive. Using geographical topic models with a set number of region centroids is easier and also allows characterization of arbitrary, unlabeled points.
- 18. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Latent Geographical Topic Analysis (LGTA) Created to identify regional topics from collections of human-tagged, geo-located photographs (Flickr) Any point on the map can be recognized as a mixture of multiple latent “geo-topics” that span the landscape Yin, Zhijun, et al. 2011. “Geographical Topic Discovery and Comparison.” Proceedings of the 20th International Conference on the World Wide Web. ACM, 2011.
- 19. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Latent Geographical Topic Analysis (LGTA) 1. Treat locations as documents containing story terms/tags
- 20. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Latent Geographical Topic Analysis (LGTA) 1. Treat locations as documents containing story terms/tags 1. Central points of place clusters stand in for the rest 1. Run pLSA math/magic on these points and their tags
- 21. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 LGTA geo-topics as regional “core samples” Lindorm / witches and Satan / hidden folk / maiden revenants: 86.88% Life cycle and calendrical rituals: 8.65% Mound dwellers, ghosts and Satan’s influence: 2.22% Things that happen in fields: 1.24%
- 22. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 ElfYelp: region geo-topics, similar regions, stories
- 23. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Kling, Christoph Carl, et al. 2014. “Detecting Non-Gaussian Geographical Topics in Tagged Photo Collections.” Proceedings of the 7th ACM International Conference on Web Search and Data Mining. ACM, 2014. Alternative: non-Gaussian geographical topics
- 24. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Next steps/future work • Enhance feature extraction for locations when building geo-topics and improve performance with multiple tags/keywords per document • Ability to query geo-topic mixtures for any point on the map (not just region centers): supported by LGTA, but not yet implemented in ElfYelp • Incorporate geographic boundaries, references to toponyms (e.g., “that hill over there”) into the model
- 25. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 ElfYelp: http://etkspace.scandinavian.ucla.edu/maps/elfyelp.html The macroscope menagerie: http://etkspace.scandinavian.ucla.edu/macroscope.html ● WitchHunter ● TrollFinder ● GhostScope ● TreasureX ● The Danish Folklore Nexus ● ...and many more Please try it out!
- 26. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 Thanks to our funders / supporters • American Council of Learned Societies • The National Endowment for the Humanities • UCLA Council on Research • Nordic Council of Ministers • The Institute for Pure and Applied Mathematics (NSF)
- 27. ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus Peter M. Broadwell, Timothy R. Tangherlini #dh2015 - University of Western Sydney - July 2, 2015 A Brief AdvertA Brief Advertisement--please apply!
- 28. Peter M. Broadwell @peterbroadwell broadwell@library.ucla.edu Timothy R. Tangherlini @tango63 tango@humnet.ucla.edu ElfYelp: Geolocated Topic Models for Pattern Discovery in a Large Folklore Corpus
Editor's Notes
- Tim: Maybe begin with a sample story showing importance of location (or not)…
- Tim: Maybe begin with a sample story showing importance of location (or not)… this is good
- Tim: Our prior work on this collection (see dh2014) has involved finding ways to make sense of a corpus that’s way too big to label by hand. Now we want to take advantage of the pervasive geographic place references in the collection to ask more questions.
- Tim: People weren’t terribly mobile then; storytellers tend to situate stories in their local environment, frequently incorporating references to nearby places. We find regions to be intrinsically more interesting than individual points; note that ETK’s collecting approach basically involved “sampling” regions based on where the good storytellers were; “good” storytellers usually told stories about their local environment.
- Tim: We’re getting at the idea that the countryside is much more complex than people have given it credit for being. Through folklore, people projected concerns about contemporary issues onto their local environment - though sometimes in the guise of elves and mound dwellers. Villages could have widely divergent ways of doing this, though conversely, sometimes villages that were very far apart might have quite similar formulations and ways of viewing the landscape (and be completely unaware of it). We want to explore this phenomenon. [Consider: both Echo Park in LA and South Delhi, India have Elf cafes! - found on Yelp and Zomato]
- Tim: ETK grouped stories with similar themes into volumes for publication; there are ~38 categories. This is a very shallow and unreliable system, but it’s a start. He also tagged each story with one of ~770 sub-categories, which are even more idiosyncratic but also more fine-grained, so they are potentially useful for computational profiling of the narrative landscape, along with actual keyword counts.
- Tim
- Tim: The idea is to use opening sample story & maybe one more to demonstrate place/term co-occurrence counts, perhaps with a map. I’ll put the slide together, or just chuck it if it’s not needed.
- Tim: The idea is to use opening sample story & maybe one more to demonstrate place/term co-occurrence counts, perhaps with a map. I’ll put the slide together, or just chuck it if it’s not needed.
- Tim: The second story used here is DS_01_0_00298 (#298), told by N Nielsen in Viborg. I also found at least one other story that was very similar to the first one, albeit in the Mound Dwellers volume (DS_01_0_01319)
- Pete: Early close reading? There are many different kinds of geographically oriented “distant reading” that we can do with this corpus. We refer to the various tools we have developed for these purposes as “macroscopes”; a macroscope is a tool for modeling and exploring highly complex systems.
- Pete: Here they are
- Pete: Plan to have this open in a browser window for a very quick demo
- Pete: Plan to have this open in a browser window for a very quick demo
- Pete: Plan to have this open in a browser window for a very quick demo
- Pete: Note that existing categories and topics built from keywords do not take geographical context into account in a generative way
- Pete: Related: Sizov, Sergej. “GeoFolk: Latent Spatial Semantics in Web 2.0 Social Media.” Proceedings of the Third ACM International Conference on Web Search and Data Mining. ACM, 2010.
- Pete
- Pete
- Pete: The output is a set of geo-topic “core samples” showing the proportions of particular topics in a given place (both the number of core samples and the number of geo-topics can be specified). These topic mixtures fan out from the core point (we use a Gaussian spatial distribution) and blend with the mixtures of the points around it. Given this model, we can “drill” a new core sample at any point on the map to see what’s there.
- Pete: This should be skipped in favor of a live demo, ideally. Demonstrate region selection, geo-topic “core sample” readout, similar places readout, info about the region (from TrollFinder), ability to drill down to story texts..
- Pete: Other, more recent projects have focused on the “location recommendation” challenge using geo-tagged social data. This approach divides the landscape into “cells” around each point and tries to find the best “cut” of the cells such that one topic predominates in the entire shape found. This is useful for marketing purposes, but not for the types of corpus and spatial exploration we’re doing; the LGTA “core sample” approach is a better fit.
- Tim: Feel free to summarize our conclusions here as well and restate the research questions this is helping us to address
- Tim