A Language and an Inference Engine for Twitter Filtering Rules Alberto Bartoli1

Barbara Carminati2

Elena Ferrari2

Eric Medvet1

1: Dipartimento di Ingegneria e Architettura, University of Trieste, Italy 2: Dipartimento di Scienze Teoriche e Applicate, Universit` a dell’Insubria, Italy

October 15th, 2016

http://machinelearning.inginf.units.it

Motivation

Table of Contents

1

Motivation

2

Language

3

Inference

4

Experimental evaluation

Bartoli et al. (UniTs+UnInsubria)

Twitter Filtering Rules

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Motivation

Motivation

People use online social networks to share huge amount of information: maybe too much? → information overload maybe disturbing/unwanted? → trolls

Twitter particularly relevant.

Bartoli et al. (UniTs+UnInsubria)

Twitter Filtering Rules

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Motivation

Recommendation, spam, filtering

Recommendation: select content to highlight that best fits user’s interests Spam: select content to hide basing on content quality Filtering: select content to hide basing on explicit user’s preferences

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Motivation

Recommendation, spam, filtering

Recommendation: select content to highlight that best fits user’s interests Spam: select content to hide basing on content quality Filtering: select content to hide basing on explicit user’s preferences

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Motivation

Contributions

Filtering: select content to hide basing on explicit user’s preferences how to specify a filtering policy?

Bartoli et al. (UniTs+UnInsubria)

Twitter Filtering Rules

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Motivation

Contributions

Filtering: select content to hide basing on explicit user’s preferences how to specify a filtering policy? → filtering language

Bartoli et al. (UniTs+UnInsubria)

Twitter Filtering Rules

October 15th, 2016

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Motivation

Contributions

Filtering: select content to hide basing on explicit user’s preferences how to specify a filtering policy? → filtering language

Writing filtering policies may be too hard for the average Twitter user, so can a policy be inferred from examples?

Bartoli et al. (UniTs+UnInsubria)

Twitter Filtering Rules

October 15th, 2016

5 / 15

Motivation

Contributions

Filtering: select content to hide basing on explicit user’s preferences how to specify a filtering policy? → filtering language

Writing filtering policies may be too hard for the average Twitter user, so can a policy be inferred from examples? → policy inference from examples

Bartoli et al. (UniTs+UnInsubria)

Twitter Filtering Rules

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Language

Table of Contents

1

Motivation

2

Language

3

Inference

4

Experimental evaluation

Bartoli et al. (UniTs+UnInsubria)

Twitter Filtering Rules

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Language

A simple model for the tweet Given: topics T = {vulgarity, religion, politics, sex, work, alcohol, school, holiday, health} post labels LP = {hasMedia, hasHashtags, hasURLs} author labels LP = {isVip}

A tweet p is given by hTPp , TAp , LpP , LpA i: TPp ⊆ T , topics of the tweet TAp ⊆ T , topics of the author of the tweet LpP ⊆ LP , post labels of the tweet LpA ⊆ LA , author labels of the author of the tweet Bartoli et al. (UniTs+UnInsubria)

Twitter Filtering Rules

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Language

Filtering policy A filtering rule r is a tuple hoTP , TPr , oTA , TAr , oLP , LrP , oLA , LrA i o∗ are set operators: ⊆ or 6⊆ TPr , TAr are (empty) set of topics LrP , LrA are (empty) set of labels A policy is a set of rules.

p is filtered by r if TPr oTP TPp ∧ TAr oTA TAp ∧ LrP oLP LpP ∧ LrA oLA LpA rule conditions are and-ed policy rules are or-ed

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Language

Example

r1 = h⊆ {vulgarity}, ⊆ ∅, ⊆ ∅, ⊆ ∅i r2 = h⊆ {politics}, 6⊆ {politics}, ⊆ ∅, ⊆ ∅i r3 = h⊆ {sex}, ⊆ ∅, ⊆ {hasMedia}, 6⊆ {isVIP}i Filters: all vulgar posts all the posts concerning politics not authored by users who usually tweet about politics all the posts concerning sex containing some media and not authored by a VIP user

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Inference

Table of Contents

1

Motivation

2

Language

3

Inference

4

Experimental evaluation

Bartoli et al. (UniTs+UnInsubria)

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Inference

Problem statement

Given: a set P+ of tweets to be filtered a set P− of tweets not to be filtered find the simplest consistent policy.

Bartoli et al. (UniTs+UnInsubria)

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Inference

Solution (sketch)

An evolutionary algorithm: a set of candidate solutions is evolved by recombining and mutating fitter solutions. custom domain-specific individual representation (individual = rule) custom domain-specific genetic operators multi-objective fitness (minimize false rejection FRR, minimize acceptance FAR, minimize rule size) separate-and-conquer strategy to compose policy

Bartoli et al. (UniTs+UnInsubria)

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Experimental evaluation

Table of Contents

1

Motivation

2

Language

3

Inference

4

Experimental evaluation

Bartoli et al. (UniTs+UnInsubria)

Twitter Filtering Rules

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Experimental evaluation

Aims, data, procedure Aims: can the language express policies of realistic complexity? can the approach infer them from examples? Data: from a large (≥ 2 · 106 ) set of tweets, after cleaning. . . 1707 tweets in English with assigned topics Procedure: 5 target policies (from 1 to 4 rules) generalization ability: policy are assessed on different sets 9 repetitions for each target policy Bartoli et al. (UniTs+UnInsubria)

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Experimental evaluation

Results

#

|ρ? |

0| |P+

0| |P−

On P+ , P− FRR FAR

test , P test On P+ − FRR FAR

1 2 3 4 5

1 1 2 3 4

110 9 196 166 32

1597 1698 1511 1541 1675

0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.06

0.00

0.00

0.00

0.01

Avg.

Bartoli et al. (UniTs+UnInsubria)

Twitter Filtering Rules

|ρ| 1 1 3 3 2

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Experimental evaluation

Results

#

|ρ? |

0| |P+

0| |P−

On P+ , P− FRR FAR

test , P test On P+ − FRR FAR

1 2 3 4 5

1 1 2 3 4

110 9 196 166 32

1597 1698 1511 1541 1675

0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.06

0.00

0.00

0.00

0.01

Avg.

|ρ| 1 1 3 3 2

policies consistent with the examples are always found

Bartoli et al. (UniTs+UnInsubria)

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Experimental evaluation

Results

#

|ρ? |

0| |P+

0| |P−

On P+ , P− FRR FAR

test , P test On P+ − FRR FAR

1 2 3 4 5

1 1 2 3 4

110 9 196 166 32

1597 1698 1511 1541 1675

0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.06

0.00

0.00

0.00

0.01

Avg.

|ρ| 1 1 3 3 2

policies consistent with the examples are always found good generalization ability

Bartoli et al. (UniTs+UnInsubria)

Twitter Filtering Rules

October 15th, 2016

15 / 15

Experimental evaluation

Results

#

|ρ? |

0| |P+

0| |P−

On P+ , P− FRR FAR

test , P test On P+ − FRR FAR

1 2 3 4 5

1 1 2 3 4

110 9 196 166 32

1597 1698 1511 1541 1675

0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.06

0.00

0.00

0.00

0.01

Avg.

|ρ| 1 1 3 3 2

policies consistent with the examples are always found good generalization ability, some errors only with the most complex target policy

Bartoli et al. (UniTs+UnInsubria)

Twitter Filtering Rules

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Experimental evaluation

Thanks!

Bartoli et al. (UniTs+UnInsubria)

Twitter Filtering Rules

October 15th, 2016

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A Language and an Inference Engine for Twitter ...

Oct 15, 2016 - People use online social networks to share huge amount of information: .... Data: from a large (≥ 2 · 106) set of tweets, after cleaning.

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