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Social Informatics

Overview of attention for book
Social Informatics
Springer, Cham

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 How Well Do Doodle Polls Do?
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    Chapter 2 Bring on Board New Enthusiasts! A Case Study of Impact of Wikipedia Art + Feminism Edit-A-Thon Events on Newcomers
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    Chapter 3 The Social Dynamics of Language Change in Online Networks
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    Chapter 4 On URL Changes and Handovers in Social Media
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    Chapter 5 Comment-Profiler: Detecting Trends and Parasitic Behaviors in Online Comments
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    Chapter 6 On Profiling Bots in Social Media
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    Chapter 7 A Diffusion Model for Maximizing Influence Spread in Large Networks
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    Chapter 8 Lightweight Interactions for Reciprocal Cooperation in a Social Network Game
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    Chapter 9 Continuous Recipe Selection Model Based on Cooking History
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    Chapter 10 Examining Community Policing on Twitter: Precinct Use and Community Response
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    Chapter 11 The Dynamics of Group Risk Perception in the US After Paris Attacks
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    Chapter 12 Determining the Veracity of Rumours on Twitter
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    Chapter 13 PicHunt: Social Media Image Retrieval for Improved Law Enforcement
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    Chapter 14 TwitterNews+: A Framework for Real Time Event Detection from the Twitter Data Stream
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    Chapter 15 Uncovering Topic Dynamics of Social Media and News: The Case of Ferguson
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    Chapter 16 Identifying Partisan Slant in News Articles and Twitter During Political Crises
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    Chapter 17 Predicting Poll Trends Using Twitter and Multivariate Time-Series Classification
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    Chapter 18 Inferring Population Preferences via Mixtures of Spatial Voting Models
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    Chapter 19 Contrasting Public Opinion Dynamics and Emotional Response During Crisis
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    Chapter 20 Social Politics: Agenda Setting and Political Communication on Social Media
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    Chapter 21 Preference-Aware Successive POI Recommendation with Spatial and Temporal Influence
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    Chapter 22 Event Participation Recommendation in Event-Based Social Networks
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    Chapter 23 An Effective Approach to Finding a Context Path in Review Texts Using Pathfinder Scaling
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    Chapter 24 How to Find Accessible Free Wi-Fi at Tourist Spots in Japan
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    Chapter 25 Mobile Communication Signatures of Unemployment
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    Chapter 26 Identifying Stereotypes in the Online Perception of Physical Attractiveness
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    Chapter 27 Analysing RateMyProfessors Evaluations Across Institutions, Disciplines, and Cultures: The Tell-Tale Signs of a Good Professor
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    Chapter 28 Detecting Coping Style from Twitter
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    Chapter 29 User Privacy Concerns with Common Data Used in Recommender Systems
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    Chapter 30 How a User’s Personality Influences Content Engagement in Social Media
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    Chapter 31 Semi-supervised Knowledge Extraction for Detection of Drugs and Their Effects
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    Chapter 32 Using Social Media to Measure Student Wellbeing: A Large-Scale Study of Emotional Response in Academic Discourse
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    Chapter 33 EmojiNet: Building a Machine Readable Sense Inventory for Emoji
Attention for Chapter 33: EmojiNet: Building a Machine Readable Sense Inventory for Emoji
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

7 X users
1 Facebook page


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Readers on

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Chapter title
EmojiNet: Building a Machine Readable Sense Inventory for Emoji
Chapter number 33
Book title
Social Informatics
Published in
Lecture notes in computer science, October 2016
DOI 10.1007/978-3-319-47880-7_33
Pubmed ID
Book ISBNs
978-3-31-947879-1, 978-3-31-947880-7

Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran


Emoji are a contemporary and extremely popular way to enhance electronic communication. Without rigid semantics attached to them, emoji symbols take on different meanings based on the context of a message. Thus, like the word sense disambiguation task in natural language processing, machines also need to disambiguate the meaning or 'sense' of an emoji. In a first step toward achieving this goal, this paper presents EmojiNet, the first machine readable sense inventory for emoji. EmojiNet is a resource enabling systems to link emoji with their context-specific meaning. It is automatically constructed by integrating multiple emoji resources with BabelNet, which is the most comprehensive multilingual sense inventory available to date. The paper discusses its construction, evaluates the automatic resource creation process, and presents a use case where EmojiNet disambiguates emoji usage in tweets. EmojiNet is available online for use at http://emojinet.knoesis.org.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 71 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 24%
Student > Master 12 17%
Student > Bachelor 7 10%
Researcher 6 8%
Student > Doctoral Student 3 4%
Other 7 10%
Unknown 19 27%
Readers by discipline Count As %
Computer Science 24 34%
Arts and Humanities 6 8%
Linguistics 6 8%
Business, Management and Accounting 4 6%
Social Sciences 4 6%
Other 7 10%
Unknown 20 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 November 2017.
All research outputs
of 22,965,074 outputs
Outputs from Lecture notes in computer science
of 8,137 outputs
Outputs of similar age
of 315,578 outputs
Outputs of similar age from Lecture notes in computer science
of 408 outputs
Altmetric has tracked 22,965,074 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 8,137 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 70% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 315,578 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 408 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.