Mining Cross-Domain Rating Datasets from Structured Data on Twitter

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Slides about mining cross-domain ratings presented at the WWW 2014 conference on April 8, in Seoul (Korea) by Simon Dooms.

Ciências

Mining Cross-Domain Rating Datasets
from Structured Data on Twitter
@sidooms
Simon Dooms

Rating Datasets
 What are ratings? Explicit user preference information
 Why ratings? Recommender systems
ConclusionCross-DomainResultsSocial SharingIntro
Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 2

Ratings Scarcity in Research
 Ratings = private data
 Public datasets to the rescue?
– MovieLens 100K (1998)
– MovieLens 1M (2000)
– MovieLens 10M (2008)
– More on recsyswiki.com
Old, Synthetic Datasets
ConclusionCross-DomainResultsSocial SharingIntro
Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 4

Social Sharing = Ratings Goldmine
 Previous research: MovieTweetings
ConclusionCross-DomainResultsSocial SharingIntro
Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 5

Social Sharing = Ratings Goldmine
 Previous research: MovieTweetings
– Movie Rating dataset from IMDb – Twitter
– https://github.com/sidooms/MovieTweetings
 What about other domains? Websites?
Well, let’s try it out!
ConclusionCross-DomainResultsSocial SharingIntro
Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 6

Target Websites - Goodreads
ConclusionCross-DomainResultsSocial SharingIntro
Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 7
Twitter user - Rating - Book title
Book author - Goodreads URL - Time

Target Websites - Pandora
ConclusionCross-DomainResultsSocial SharingIntro
Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 8
Twitter user - Song
Pandora URL - Time

Target Websites - YouTube
ConclusionCross-DomainResultsSocial SharingIntro
Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 9
Twitter user - (Video uploader)
YouTube URL - Time

Mining Experiment
 But words are wind…
– 2 Weeks experiment
– 4 Online platforms
ConclusionCross-DomainResultsSocial SharingIntro
Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 10

ConclusionCross-DomainResultsSocial SharingIntro
Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 12
Python code + Task Scheduler = Dataset files
https://github.com/sidooms/Twitter-ratings

The Numbers
One more thing …
ConclusionCross-DomainResultsSocial SharingIntro
Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 13

Cross-Domain Rating Dataset
ConclusionCross-DomainResultsSocial SharingIntro
Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 14

Applications
 Collect ratings for recsys research / input
 Cross-domain recsys research
 Trend detection, analytics, ...
 Applicable for all social sharing webs
ConclusionCross-DomainResultsSocial SharingIntro
Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 15

Conclusions
 Ratings scarcity in research
 Public dataset are old and synthetic
 Social sharing = ratings goldmine
 2 week experiment, 4 major websites
 Python code & datasets on Github
 True cross-domain ratings dataset
ConclusionCross-DomainResultsSocial SharingIntro
Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 16

@sidooms
Simon Dooms
Mining Cross-Domain Rating Datasets
from Structured Data on Twitter

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A botany file is similar to a chemistry practical file but specifically focuses on experiments and observations related to plants. It typically includes detailed descriptions of plant-related experiments, such as germination studies, growth experiments, tissue culture techniques, plant anatomy investigations, and ecological surveys. The file may contain records of plant specimens collected, their classification, characteristics, and ecological significance. Additionally, it may include diagrams, photographs, drawings, tables, and data analysis related to plant research. Like a chemistry practical file, a botany file serves as a documentation of practical work conducted in the field of botany and is often used for assessment and educational purposes.

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Bollworms are among the most damaging pests in cotton cultivation, affecting the bolls where the cotton fibers are formed. There are several species of bollworms, each capable of causing significant yield loss and quality degradation if not effectively managed. Here’s a detailed look at the primary bollworm species affecting cotton: Cotton Bollworm (Helicoverpa armigera): Also known as the corn earworm or the Old World bollworm, this pest is found in many regions around the world. It is highly polyphagous (feeds on many different plants) and poses a threat not only to cotton but also to maize, tomatoes, and legumes. The larvae bore into the cotton bolls, feeding on the developing seeds and fibers, which can lead to boll rot. Pink Bollworm (Pectinophora gossypiella): A significant pest of cotton, the pink bollworm larvae infest the cotton bolls, feeding on the seeds and lint. This can severely damage or destroy the bolls. In regions where pink bollworms are prevalent, they have been a major driver for the adoption of genetically engineered Bt cotton, which expresses a bacterium gene toxic to certain insects. Tobacco Budworm (Heliothis virescens): Closely related to the cotton bollworm, the tobacco budworm primarily attacks tobacco but is also a common pest in cotton. It primarily damages the flowers and bolls of the cotton plant. Differentiating between the tobacco budworm and the cotton bollworm based on appearance can be challenging, but it is crucial for effective management. American Bollworm (Helicoverpa zea): Known in some regions as the corn earworm, it is similar in behavior to Helicoverpa armigera and poses a threat to a variety of crops, including cotton. The larvae attack the cotton bolls, leading to direct damage to the cotton lint and seeds. Management Strategies: Cultural Controls: Crop rotation, destruction of crop residues, and deep plowing can help break the pest’s life cycle. Timing of planting can also be adjusted to avoid peak pest infestation. Biological Controls: Natural enemies like Trichogramma wasps, which parasitize bollworm eggs, and predators such as lacewings and ladybugs can be encouraged. Bacillus thuringiensis (Bt) products can also be sprayed, which are particularly effective against young larvae. Chemical Controls: Insecticides may be required when infestation levels exceed economic thresholds. However, resistance management must be considered, alternating modes of action to avoid developing resistance. Genetic Approaches: Bt cotton, genetically modified to express Bacillus thuringiensis toxin, has been highly effective in controlling bollworms and has dramatically reduced the reliance on chemical insecticides. Monitoring and Scouting: Regular field scouting and using pheromone traps to monitor adult populations can help in timely and targeted application of control measures. The effective management of bollworms often requires an integrated approach

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Cotton crops are vulnerable to a variety of sucking pests, which can severely impact plant health, yield, and fiber quality. These pests primarily feed on plant sap, extracting nutrients directly from the plant's vascular system. Here's a breakdown of some of the most significant sucking pests in cotton cultivation: Aphids: Cotton aphids or melon aphids can cause direct damage by sucking sap and indirect damage by secreting honeydew, which encourages sooty mold growth. This can interfere with photosynthesis and weaken the plant. Aphids can also transmit viral diseases. Whiteflies: Two species, the silverleaf whitefly and the bandedwinged whitefly, are particularly troublesome. They not only suck sap from the underside of leaves, causing yellowing and leaf drop, but their honeydew excretion promotes sooty mold and they can transmit several plant viruses. Thrips: While thrips can chew on plants, their primary damage to cotton is through sucking. They attack the cotton plant during its seedling stage, which can stunt growth and reduce vigor. Thrips are also capable of transmitting the Cotton Bud disease. Spider Mites: These are not insects but arachnids. Spider mites, such as the two-spotted spider mite, suck cell contents from the leaves, leading to speckled discoloration and potentially significant leaf loss if infestations are severe. Leafhoppers: Including various species, leafhoppers can cause direct damage through feeding, which results in leaf curling and stunted growth. They can also be vectors for plant diseases. Mealybugs: These pests are less common but can be problematic, especially in clustered planting conditions. They suck sap and secrete honeydew, which leads to sooty mold. Mealybugs can also spread viruses. Stink Bugs: Although primarily known for their chewing mouthparts, certain stink bugs can cause damage similar to sucking pests by injecting saliva into the plant and sucking out nutrients, leading to boll damage and stained lint. Management Strategies: Cultural Controls: This includes practices such as crop rotation, using resistant varieties, and managing planting and harvesting times to avoid peak pest populations. Biological Controls: Beneficial insects like lady beetles, lacewings, and predatory mites can naturally control sucking pest populations. Parasitic wasps also play a role in controlling aphid and whitefly populations. Chemical Controls: Insecticides may be used but should be chosen carefully to minimize resistance development and preserve beneficial insects. Systemic insecticides can be particularly effective against sucking pests. Integrated Pest Management (IPM): Combining multiple control strategies based on monitoring and thresholds to apply the most effective and environmentally sensitive approach. Effective management of sucking pests in cotton requires a thorough understanding of the pest species present, their life cycles, and the ecological balance of the field environment.

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Mining Cross-Domain Rating Datasets from Structured Data on Twitter

1. Mining Cross-Domain Rating Datasets from Structured Data on Twitter @sidooms Simon Dooms

2. Rating Datasets  What are ratings? Explicit user preference information  Why ratings? Recommender systems ConclusionCross-DomainResultsSocial SharingIntro Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 2

3. Rating Datasets  What are ratings? Explicit user preference information  Why ratings? Recommender systems ConclusionCross-DomainResultsSocial SharingIntro Apr. 08, 2014 3

4. Ratings Scarcity in Research  Ratings = private data  Public datasets to the rescue? – MovieLens 100K (1998) – MovieLens 1M (2000) – MovieLens 10M (2008) – More on recsyswiki.com Old, Synthetic Datasets ConclusionCross-DomainResultsSocial SharingIntro Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 4

5. Social Sharing = Ratings Goldmine  Previous research: MovieTweetings ConclusionCross-DomainResultsSocial SharingIntro Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 5

6. Social Sharing = Ratings Goldmine  Previous research: MovieTweetings – Movie Rating dataset from IMDb – Twitter – https://github.com/sidooms/MovieTweetings  What about other domains? Websites? Well, let’s try it out! ConclusionCross-DomainResultsSocial SharingIntro Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 6

7. Target Websites - Goodreads ConclusionCross-DomainResultsSocial SharingIntro Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 7 Twitter user - Rating - Book title Book author - Goodreads URL - Time

8. Target Websites - Pandora ConclusionCross-DomainResultsSocial SharingIntro Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 8 Twitter user - Song Pandora URL - Time

9. Target Websites - YouTube ConclusionCross-DomainResultsSocial SharingIntro Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 9 Twitter user - (Video uploader) YouTube URL - Time

10. Mining Experiment  But words are wind… – 2 Weeks experiment – 4 Online platforms ConclusionCross-DomainResultsSocial SharingIntro Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 10

11.

12. ConclusionCross-DomainResultsSocial SharingIntro Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 12 Python code + Task Scheduler = Dataset files https://github.com/sidooms/Twitter-ratings

13. The Numbers One more thing … ConclusionCross-DomainResultsSocial SharingIntro Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 13

14. Cross-Domain Rating Dataset ConclusionCross-DomainResultsSocial SharingIntro Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 14

15. Applications  Collect ratings for recsys research / input  Cross-domain recsys research  Trend detection, analytics, ...  Applicable for all social sharing webs ConclusionCross-DomainResultsSocial SharingIntro Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 15

16. Conclusions  Ratings scarcity in research  Public dataset are old and synthetic  Social sharing = ratings goldmine  2 week experiment, 4 major websites  Python code & datasets on Github  True cross-domain ratings dataset ConclusionCross-DomainResultsSocial SharingIntro Apr. 08, 2014 Simon Dooms - Ghent University - MSM 2014 16

17. @sidooms Simon Dooms Mining Cross-Domain Rating Datasets from Structured Data on Twitter

Mining Cross-Domain Rating Datasets from Structured Data on Twitter

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