Location prediction
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As the popularity of online social networking sites such as Twitter and Facebook continues to rise, the volume of textual content generated on the web is increasing rapidly. The mining of user generated content in social media has proven effective in domains ranging from personalization and recommendation systems to crisis management. These applications stand to be further enhanced by incorporating information about the geo-position of social media users in their analysis. Due to privacy concerns, users are largely reluctant to share their location information. As a consequence of this, researchers have focused on automatic inferring of location information from the contents of a user's tweets. Existing approaches are purely data-driven and require large training data sets of geo-tagged tweets. Furthermore, these approaches rely solely on social media features or probabilistic language models and fail to capture the underlying semantics of the tweets. In this thesis, we propose a novel knowledge based approach that does not require any training data. Our approach uses Wikipedia, a crowd sourced knowledge base, to extract entities that are relevant to a location. We refer to these entities as local entities. Additionally, we score the relevance of each local entity with respect to the city, using the Wikipedia Hyperlink Graph. We predict the most likely location of the user by matching the scored entities of a city and the entities mentioned by users in their tweets. We evaluate our approach on a publicly available dataset consisting of 5119 Twitter users across continental United States and show comparable accuracy to the state-of-the-art approaches. Our results demonstrate the ability to pinpoint the location of a Twitter user to a state and a city using Wikipedia, without needing to train a probabilistic model.
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Location prediction
- 1. Knowledge Enabled Location Prediction of Twitter Users Master’s Thesis Revathy Krishnamurthy Committee Amit P. Sheth (Advisor) Krishnaprasad Thirunarayan Derek Doran Collaborator Pavan Kapanipathi 1
- 2. Background Knowledge can improve a machine’s ability to interpret text BUCKEYE STATE 2
- 4. Geographic footprint of a Twitter user 4
- 5. News Recommender Systems Beavercreek preschool to open in 2015 By Sharon D. Boykin A $5.1 million preschool in Beavercreek city Schools district will help accommodate a growing of student population and reduce overcrowding, according to school officials. Ohio’s health exchange to include more competition By Randy Tucker It was just a year ago that the insurance industry fretted over potential loses from the new insurance market created by Affordable Care Act. Recommended for you WHY IS LOCATION IMPORTANT? • Targeted advertising • Opinion Analysis • Disaster Response • Location Based Services Other applications 5
- 6. Geo-tagged Tweets Profile Information LOCATION PUBLISHED BY USER 6
- 7. Geo-tagged Tweets Profile Information LOCATION PUBLISHED BY USER • Less than 4% of tweets contain geo-spatial tags • Location field in profile is either empty or contains invalid information such as “Justin Bieber’s heart” 7
- 8. Friends INFERRING LOCATION OF A TWITTER USER Followees 8 Just drove around Golden Gate Park two times trying to get in Cleveland Browns confuse me. When I give up on them, they actually show up to play. Followers Network based Content based
- 9. Friends NETWORK BASED APPROACHES FollowersFollowees Depends on the friends and followers of a user whose location is known 9
- 10. CONTENT BASED APPROACHES Just drove around Golden Gate Park two times trying to get in Cleveland Browns confuse me. When I give up on them, they actually show up to play. • Supervised Approaches • Probabilistic Models – (Cheng, Caverlee, and Lee, 2010) • Cascading Topic Models – (Eisenstein, Connor, Smith, and Xing, 2010) • Gaussian Mixture Model – (Chang, Lee, Eltaher, and Lee, 2012) • Language Models – (Doran, Gokhale, and Dagnino, 2014) • Ensemble of Statistical and Heuristic Classifiers – (Mahmud, Nichols, and Drews, 2014) 10 Geographic location of a user influences the contents of their tweets
- 11. Content-based approach APPROACHES TO LOCATE A TWITTER USER Reference: Cheng, Caverlee, and Lee, 2010 11
- 12. Content-based approach APPROACHES TO LOCATE A TWITTER USER 12 Reference: Cheng, Caverlee, and Lee, 2010
- 13. PROBLEM STATEMENT 13 Predict the location of a Twitter user based on their tweets, by exploiting Wikipedia to create a location specific knowledgebase
- 14. • Knowledge-enabled approach to predict the location of Twitter users based on the contents of their tweets without using any training dataset of geo-tagged tweets • Creation of location specific knowledgebase extracted from Wikipedia by introducing the concept of Local Entities • Evaluation of the approach on a publicly available dataset with 55% accuracy and 429 miles of Average Error Distance CONTRIBUTIONS 14
- 15. KNOWLEDGE-BASE ENABLED APPROACH San Francisco: Golden Gate Bridge, San Francisco 49ers, San Francisco Chronicle … Entity Count Golden Gate Bridge 4 San Francisco 49ers 2 San Francisco Chronicle 1 Top-k predictions: San Francisco Oakland Palo Alto 15
- 16. KNOWLEDGE BASE GENERATOR Internal Links Extraction LocalEntity-1 LocalEntity-2 --- LocalEntity-n city-1 city-2 city-k Weighted Local Entities Entity Recognition and Scoring Annotated Tweets USER PROFILE GENERATOR LOCATION PREDICTION Location Predictor Ranked cities for user KNOWLEDGE-BASE ENABLED APPROACH 16
- 17. SAN FRANCISCO NEW YORK CITY HOUSTON LOCAL ENTITIES 17
- 18. • Collaborative encyclopedia • As of 2014, English Wikipedia has 4.6 million articles, 18 billion pages views and 500 million unique visitors per month. • Category Structure • Used for document clustering, tweet classification, personalization systems etc. • At Kno.e.sis, used in applications such as • Doozer (Thomas, Mehra, Brooks, and Sheth, 2008) • BLOOMS (Jain, Hitzler, Sheth, Verma, and Yeh, 2010) • Hierarchical Interest Graph (Kapanipathi, Jain, Venkataramani, and Sheth, 2014) • Link Structure • Used for word sense disambiguation, semantic relatedness between terms etc. WIKIPEDIA 18
- 19. LINK STRUCTURE OF WIKIPEDIA 19
- 20. LINK STRUCTURE OF WIKIPEDIA 20
- 21. “In general, links should be created to relevant connections to the subject of another article that will help readers understand the article more fully. This can include people, events, and topics that already have an article or that clearly deserve one, so long as the link is relevant to the article in question.” Source: http://en.wikipedia.org/wiki/Help:Link#Wikilinks LINK STRUCTURE OF WIKIPEDIA 21
- 22. • We consider the internal links of location pages as Local Entities of the city Local Entities of San Francisco LOCAL ENTITIES • While a city does not contain link to itself, we use the city as a local entity 22
- 23. LOCAL ENTITIES San Francisco, California – 717 local entities Fairborn, Ohio – 110 local entities 23
- 24. ARE ALL ENTITIES EQUALLY LOCAL? 24
- 25. ARE ALL ENTITIES EQUALLY LOCAL? 25 San Francisco Chronicle San Francisco ExaminerSF Weekly MSNBC CNN BBC Al Jazeera America
- 26. • Pointwise Mutual Information – standard measure of association between two variables • Assumption is that higher is the localness of an entity with respect to the city, higher will be the statistical dependence between them • Computed as: 𝑃𝑀𝐼 𝑐, 𝑒 = 𝑙𝑜𝑔2 𝑃 𝑐,𝑒 𝑃 𝑐 .𝑃(𝑒) Association-based Measure LOCALNESS MEASURE OF ENTITIES 26
- 27. Graph-based Measure LOCALNESS MEASURE OF ENTITIES 27 The Boston Red Sox, a founding member of the American League of Major League Baseball in 1901.. Boston Red Sox The Boston Red Sox are an American professional baseball team based in Boston, Massachusetts ... They are members of American League (AL). Boston American League
- 28. LOCALNESS MEASURE OF ENTITIES 28 Directed Graph of Local Entities of Boston
- 29. • Betweenness Centrality (BC) – Measures the importance of a node relative to the rest of the nodes in the graph • A high BC score of a vertex in a graph indicates that it lies on considerable fraction of shortest path connecting others • Computed as: 𝐶 𝐵 𝑐, 𝑒 = 𝑒𝑖 ≠𝑒≠𝑒𝑗 𝜎 𝑒𝑖𝑒𝑗 (𝑒) 𝜎 𝑒𝑖𝑒𝑗 Graph-based Measure LOCALNESS MEASURE OF ENTITIES 29
- 30. LOCALNESS MEASURE OF ENTITIES 30 Directed Graph of Local Entities of Boston Boston Red Sox: 0.004540 American League: 0.000046
- 31. Alcatraz Island Treasure Island Alameda Island Financial District Market Street Fisherman’s Wharf San Francisco 49ers Cow Hollow Silicon Valley South Beach …. Suspension Bridge Hyde Street Pier Irving Morrow Angelo Rossi Art Deco Charles Alton Ellis Bethlehem Steel Half Way to Hell Club International Orange … San Francisco Bay Golden Gate San Francisco Chronicle U.S. Route 101 Marin County Sausalito Bay Area … Semantic Overlap Measure LOCALNESS MEASURE OF ENTITIES 31
- 32. • Measures the relatedness between concepts with the intuition that related concepts are connected to similar entities • Jaccard Index: Overlap between two sets 𝑗𝑎𝑐𝑐𝑎𝑟𝑑 𝑐, 𝑒 = |𝑂 𝑐 ∩𝑂 𝑒 | |𝑂 𝑐 ∪𝑂 𝑒 | Semantic Overlap Measure LOCALNESS MEASURE OF ENTITIES 32
- 33. • Tversky Index: Asymmetric similarity measure between two sets 𝑡𝑖 𝑐, 𝑒 = |𝑂 𝑐 ∩𝑂 𝑒 | 𝑂 𝑐 ∩𝑂 𝑒 + α 𝑂 𝑐 −𝑂 𝑒 + β|𝑂 𝑒 −𝑂 𝑐 | • We choose α = 0 and β = 1 • For every entity in the page of a local entity not found in the page of the city, penalize the local entity Semantic Overlap Measure LOCALNESS MEASURE OF ENTITIES 33
- 34. KNOWLEDGE-BASE OF LOCAL ENTITIES Local Entities of San Francisco (Localness measure: Tversky Index) 34
- 35. KNOWLEDGE BASE GENERATOR Internal Links Extraction LocalEntity-1 LocalEntity-2 --- LocalEntity-n city-1 city-2 city-k Weighted Local Entities Entity Recognition and Scoring Annotated Tweets USER PROFILE GENERATOR LOCATION PREDICTION Location Predictor Ranked cities for user KNOWLEDGE-BASE ENABLED APPROACH 35
- 36. Step 1: Entity Linking Just drove around Golden Gate Park trying to get in. CREATION OF USER PROFILE We use Zemanta for Entity Linking 36
- 37. Step 1: Entity Linking Just drove around Golden Gate Park trying to get in. CREATION OF USER PROFILE Entity Count Golden Gate Bridge 4 San Francisco 49ers 2 San Francisco Chronicle 1 User Profile for user 𝑢 defined as: 𝑃 𝑢 = 𝑒, 𝑠 𝑒 ∈ 𝑊, 𝑠 ∈ 𝑅} Step 2: Entity Scoring We use Zemanta for Entity Linking 37
- 38. KNOWLEDGE BASE GENERATOR Internal Links Extraction LocalEntity-1 LocalEntity-2 --- LocalEntity-n city-1 city-2 city-k Weighted Local Entities Entity Recognition and Scoring Annotated Tweets USER PROFILE GENERATOR LOCATION PREDICTION Location Predictor Ranked cities for user KNOWLEDGE-BASE ENABLED APPROACH 38
- 39. LOCATION PREDICTION • Compute an aggregate score for each city whose local entities are found in a user’s tweets 𝑙𝑜𝑐𝑆𝑐𝑜𝑟𝑒 𝑐, 𝑢 = 𝑗=1 𝐼 𝑐𝑢 𝑙𝑜𝑐𝑙 𝑐, 𝑒𝑗 × 𝑠𝑒𝑗 where 𝐼 𝑐𝑢 are local entities of city 𝑐 found in tweets of user 𝑢 , 𝑒𝑗 ∈ 𝐼𝑐𝑢 and 𝑙𝑜𝑐𝑙(𝑐, 𝑒𝑗) is the localness score of entity 𝑒𝑗 with respect to city 𝑐 • Rank 𝑙𝑜𝑐𝑆𝑐𝑜𝑟𝑒 𝑐, 𝑢 in descending order to predict the top-k locations of a user 39
- 40. San Francisco International Airport (6), San Francisco (4), Nob Hill (3), San Francisco Museum of Modern Art (1), Beach Blanket Babylon (2), San Francisco Municipal Railway (4), Golden Gate Park (1), San Francisco Bay Area (1), SF Weekly (1), Fox Oakland Theatre (2), Berkley (1), Green Day (1), Oakland (9), San Francisco Bay Area (1), The White Stripes (1), Detroit Metropolitan Wayne County Airport (1), Detroit Historical Museum (1), Detroit Red Wings (4), General Motors (1), Palo Alto (6), SAP AG (8), Facebook (3), PARC (company) (2), Dell (1), Google (1), … LOCATION PREDICTION User Profile Knowledgebase Nob Hill 0.48214 SF Weekly 0.1875 Golden Gate Park 0.16783 San Francisco International Airport 0.06818 … Fox Oakland Theatre 0.09375 SF Bay Area 0.12972 Green Day 0.02066 … Detroit Historical Museum 0.4838 General Motors 0.05538 Detroit Red Wings 0.0232 … PARC (company) 0.03726 Google 0.04678 Facebook 0.05810 San Francisco Oakland, CA Detroit, MI Palo Alto, CA 40
- 41. LOCATION PREDICTION San Francisco International Airport (6), San Francisco (4), Nob Hill (3), San Francisco Museum of Modern Art (1), Beach Blanket Babylon (2), San Francisco Municipal Railway (4), Golden Gate Park (1), San Francisco Bay Area (1), SF Weekly (1) 14.5531 Fox Oakland Theatre (2), Berkley (1), Green Day (1), Oakland (9), San Francisco Bay Area (1) 10.7584 The White Stripes (1), Detroit Metropolitan Wayne County Airport (1), Detroit Historical Museum (1), Detroit Red Wings (4), General Motors (1) 8.0600 Palo Alto (6), SAP AG (8), Facebook (3), PARC (company) (2), Dell (1), Google (1) 6.9175 User Profile Knowledgebase Location Prediction Nob Hill 0.48214 SF Weekly 0.1875 Golden Gate Park 0.16783 San Francisco International Airport 0.06818 … Fox Oakland Theatre 0.09375 SF Bay Area 0.12972 Green Day 0.02066 … Detroit Historical Museum 0.4838 General Motors 0.05538 Detroit Red Wings 0.0232 … PARC (company) 0.03726 Google 0.04678 Facebook 0.05810 San Francisco Oakland, CA Detroit, MI Palo Alto, CA 41
- 42. • All cities of United States with population > 5000 as published in census estimates of 2012 • 4,661 cities and 500714 local entities Knowledge base IMPLEMENTATION Baseline • Considers all local entities to be equally local to the city • Location prediction based only on frequency of entities 42
- 43. • Published by Cheng, Caverlee, and Lee, 2010. • Contains 5119 active users from continental United States with approximately 1000 tweets per user. • User’s location listed in the form of latitude and longitude. Test Dataset EVALUATION 43
- 44. • Error Distance 𝐸𝑟𝑟𝑜𝑟𝐷𝑖𝑠𝑡 𝑢 = 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑙𝑜𝑐𝑎𝑐𝑡 𝑢 , 𝑙𝑜𝑐𝑒𝑠𝑡 𝑢 Distance between actual location of the user and the estimated location • Average Error Distance 𝐴𝐸𝐷 𝑈 = 𝑢∈𝑈 𝐸𝑟𝑟𝑜𝑟𝐷𝑖𝑠𝑡(𝑢) |𝑈| Average of error distance of all users in the test dataset • Accuracy 𝐴𝐶𝐶 𝑈 = |{𝑢|𝑢∈𝑈 ˄ 𝐸𝑟𝑟𝑜𝑟𝐷𝑖𝑠𝑡 𝑢 ≤100}| |𝑈| Percentage of users predicted within 100 miles of their actual location Evaluation Metrics EVALUATION 44
- 45. Location Prediction Results EVALUATION Localness Measure ACC (%) AED (in Miles) ACC@2 ACC@3 ACC@5 Baseline 25.21 632.56 38.01 42.78 47.95 PMI 38.48 599.40 49.85 56.06 64.15 BC 47.91 478.14 57.39 62.18 66.98 Jaccard Index 53.21 433.62 67.41 73.56 78.84 Tversky Index 54.48 429.00 68.72 74.68 79.99 45
- 46. EVALUATION Localness Measure ACC (%) AED (in Miles) ACC@2 ACC@3 ACC@5 Baseline 25.21 632.56 38.01 42.78 47.95 PMI 38.48 599.40 49.85 56.06 64.15 BC 47.91 478.14 57.39 62.18 66.98 Jaccard Index 53.21 433.62 67.41 73.56 78.84 Tversky Index 54.48 429.00 68.72 74.68 79.99 • PMI is not normalized hence sensitive to the count of the occurrences of local entities in the Wikipedia corpus • E.g. PMI of local entities of Glenn Rock, New Jersey is higher than those of San Francisco 46
- 47. EVALUATION Localness Measure ACC (%) AED (in Miles) ACC@2 ACC@3 ACC@5 Baseline 25.21 632.56 38.01 42.78 47.95 PMI 38.48 599.40 49.85 56.06 64.15 BC 47.91 478.14 57.39 62.18 66.98 Jaccard Index 53.21 433.62 67.41 73.56 78.84 Tversky Index 54.48 429.00 68.72 74.68 79.99 • Does a good job of assigning low scores to common entities. • E.g. community college, National Weather Service, start up company etc. • Fails for entities with some relevance to the city but no distinguishing factor • E.g. IBM with respect to Endicott, New York 47
- 48. LOCALNESS MEASURE OF ENTITIES 48
- 49. EVALUATION Localness Measure ACC (%) AED (in Miles) ACC@2 ACC@3 ACC@5 Baseline 25.21 632.56 38.01 42.78 47.95 PMI 38.48 599.40 49.85 56.06 64.15 BC 47.91 478.14 57.39 62.18 66.98 Jaccard Index 53.21 433.62 67.41 73.56 78.84 Tversky Index 54.48 429.00 68.72 74.68 79.99 • Underperforms for local entities with fewer entities than the city • E.g. Eureka Valley and California with respect to San Francisco. 49
- 51. EVALUATION Localness Measure ACC (%) AED (in Miles) ACC@2 ACC@3 ACC@5 Baseline 25.21 632.56 38.01 42.78 47.95 PMI 38.48 599.40 49.85 56.06 64.15 BC 47.91 478.14 57.39 62.18 66.98 Jaccard Index 53.21 433.62 67.41 73.56 78.84 Tversky Index 54.48 429.00 68.72 74.68 79.99 • Best performing localness measure • Overcomes the disadvantage of Jaccard Index. • For example: We are able to assign higher localness to Eureka Valley (0.7096) than California (0.1270) with respect to San Francisco 51
- 53. Top-k Average Error Distance EVALUATION 53
- 54. Distribution of all users in the dataset Distribution of accurately predicted users Distribution of users 54
- 55. Comparison with Existing Approaches EVALUATION Method ACC (%) AED (in miles) Cheng, Caverlee, and Lee, 2010 51.00 535.56 Chang, Lee, Eltaher, and Lee, 2012 49.9 509.3 Wikipedia based Approach 54.48 429.00 55
- 56. Impact of Local Entities EVALUATION 56
- 57. Top 100 Cities EVALUATION • 2172 users from the dataset are from the top-100 most populated cities of United States • 60% users predicted within 100 miles of their actual location • 54% users predicted exactly at the city level 57
- 58. CONCLUSION • Presented a crowd sourced knowledge based approach, that does not require geo-tagged tweets as a training dataset, to predict the location of a user • Introduced the concept of Local Entities and preprocessed Wikipedia Hyperlink Graph to extract local entities for each city • Investigated relatedness measures to establish the degree of association between a local entity and a city • Evaluated the proposed approach against a benchmark dataset published by Cheng et al. For 5119 users, we are able to predict the location of 55% of users within 100 miles with an average error distance of 429 miles 58
- 59. FUTURE WORK • Compute the confidence score of the prediction based on top-k cities and count of local entities in tweets • Investigate other localness measures for score local entities • Consider semantic types, categories of local entities and weight the contribution based on types • Explore other knowledge bases such as Wikitravel and GeoNames 59
- 60. ACKNOWLEDGEMENTS THANK YOU! Amit P. Sheth Krishnaprasad Thirunarayan Derek Doran 60
Editor's Notes
- “Buckeye State” – Nickname of Ohio
- Location provides “context”
- Users can publish geographic information through cellphone or profile
- Cheng et al. found 21% users contained location as granular as city, state in their profile There was a need to automatically infer the location of a user
- Network based approaches are based on the network of a Twitter user
- he general idea behind these approaches is to determine the probabilistic distribution of words across a region
- Cheng et al. proposed a probabilistic framework to model the spatial distribution of words. They proposed the concept of local words which are words that have a compact geographic scope.
- Weakness: Large training dataset required. Collection process of geo-tagged tweets is time intensive. Does not exploit underlying semantics in tweets
- My thesis addresses weaknesses of existing approaches by using a knowledge-based approach to extract location specific concepts from Wikipedia
- Brief description of the approach
- Brief description of the approach
- Local Entities: Entities that have a high relatedness to a city and can discriminate between geographic locations
- Our work is based on the link structure of Wikipedia
- Links only to topically relevant entities
- Previous research have concluded that location names provide important clues to the location of a user
- Number of local entities for each city varies.
- Association based measure: Compute relatedness based on their occurrences in a large corpus
- Construct a directed graph of local entities for each city
- Construct a graph of local entities for each city Number of Nodes: 474 Number of Edges: 5921
- Based on the idea that higher is the overlap between concepts found in the Wikipedia pages of a city and an entity higher is the degree of localness of the entity
- Local Entities of San Francisco represented in a tag cloud weighted based on Tversky Index
- The next module is the user profile generator. We create a semantic profile of each user consisting of Wikipedia entities found in their tweets
- Brief description of the approach
- For predicting the location of a user we compute an aggregate score for each city whose local entities are found in a user’s tweets.
- Cheng, Caverlee, and Lee, 2010
- Introduction to Twitter and location of a Twitter user