Intent Mining for the Good, Bad, and Ugly Use of Social Web: Concepts, Methods, and Challenges

Purohit, Hemant; Pandey, Rahul

doi:10.1007/978-3-319-94105-9_1

Cited by 12 publications

(8 citation statements)

References 41 publications

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“…Query search is one of the most important domains in IARS, which extracts implicit user intent in the user-supplied query. 72,[168][169][170][171][172][173][174][175] These techniques employ click behaviors, text analysis, sequence mapping, and temporal techniques for optimally modeling query intent. Query classification is different from general text classification as queries are ambiguous, short, and might belong to many categories simultaneously.…”

Section: Intent-aware Query Predictionmentioning

confidence: 99%

“…Intent-aware algorithm selection has been a promising area of research during the past few years. 174,[213][214][215][216][217] Different recommendation algorithms can be used based on user intentions. For example, suppose the user carefully probes an item.…”

Section: Intent-aware Algorithm Selectionmentioning

confidence: 99%

“…Query search is one of the most important domains in IARS, which extracts implicit user intent in the user‐supplied query 72,168‐175 . These techniques employ click behaviors, text analysis, sequence mapping, and temporal techniques for optimally modeling query intent.…”

Section: Taxonomy Of Iars Applications and Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

Developing smart city services using intent‐aware recommendation systems: A survey

Rafique

Hafid

Qadir

2023

Trans Emerging Tel Tech

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Smart cities could be defined as urban areas that use Information and Communication Technology (ICT) to solve city problems in efficient and sustainable ways. Intent-aware Recommender Systems (IARS) within ICT play a crucial role in filtering useless information according to user demands and assist in decision-making in various smart city platforms. In smart cities, the user traces on IoT, RFIDs, mobiles, and smart sensors capture actual user intent of performing an activity and enhance user satisfaction by proposing optimal services. This paper presents a detailed literature survey of the field of IARS and how it can be used for developing smart city services. First, we present the evolution of IARS with the development of computing technology. Then, we present case studies, synergies, advances, and a reference implementation architecture of IARS for smart cities. We discuss requirements for developing smart city services using IARS. Furthermore, we devise a comprehensive taxonomy of applications and techniques of IARS using different performance parameters. Finally, we elaborate on current issues, challenges, and future research directions in IARS; these directions we believe will pave the way for autonomous service provisioning in smart cities.

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Section: Intent-aware Query Predictionmentioning

confidence: 99%

Section: Intent-aware Algorithm Selectionmentioning

confidence: 99%

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Developing smart city services using intent‐aware recommendation systems: A survey

Rafique

Hafid

Qadir

2023

Trans Emerging Tel Tech

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“…However, mining relevant user intentions from the content of social media messages is challenging, given the complexity of natural language understanding – one of the key problems of artificial intelligence. 60,61 For example, in social media environments, individuals and groups will express their intent for needs and information in varying ways based upon culture, language, and colloquialisms. It is an emerging research area with multiple challenges in efficiently mining the sense from the content with two major issues.…”

Section: Future Research Challenges To Extract Relevant Information Fmentioning

confidence: 99%

Challenges to Transforming Unconventional Social Media Data into Actionable Knowledge for Public Health Systems During Disasters

Chan¹,

Purohit

2019

Disaster med. public health prep.

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Every year, there are larger and more severe disasters and health organizations are struggling to respond with services to keep public health systems running. Making decisions with limited health information can negatively affect response activities and impact morbidity and mortality. An overarching challenge is getting the right health information to the right health service personnel at the right time. As responding agencies engage in social media (eg, Twitter, Facebook) to communicate with the public, new opportunities emerge to leverage this non-traditional information for improved situational awareness. Transforming these big data is dependent on computers to process and filter content for health information categories relevant to health responders. To enable a more health-focused approach to social media analysis during disasters, 2 major research challenges should be addressed: (1) advancing methodologies to extract relevant information for health services and creating dynamic knowledge bases that address both the global and US disaster contexts, and (2) expanding social media research for disaster informatics to focus on health response activities. There is a lack of attention on health-focused social media research beyond epidemiologic surveillance. Future research will require approaches that address challenges of domain-aware, including multilingual language understanding in artificial intelligence for disaster health information extraction. New research will need to focus on the primary goal of health providers, whose priority is to get the right health information to the right medical and public health service personnel at the right time.

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“…The approach was to exploit historical user data from search logs and click sequence graphs for broad categories of navigational, informational, and transactional intent [19]. It has been a relatively new area of investigation in social media research [20], where recent works have investigated the intent classification problem for social media text in specific domains such as buying-selling intent for commercial products [21] and help seeking-offering intent during disasters [22] as well as in general, across different topics for recommendations such as travel and food [23].…”

Section: B Modeling User Intent On Social Mediamentioning

confidence: 99%

Distributional Semantics Approach to Detect Intent in Twitter Conversations on Sexual Assaults

Pandey

Purohit

Stabile

2018

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)

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The recent surge in women reporting sexual assault and harassment (e.g., #metoo campaign) has highlighted a longstanding societal crisis. This injustice is partly due to a culture of discrediting women who report such crimes and also, rape myths (e.g., 'women lie about rape'). Social web can facilitate the further proliferation of deceptive beliefs and culture of rape myths through intentional messaging by malicious actors.This multidisciplinary study investigates Twitter posts related to sexual assaults and rape myths for characterizing the types of malicious intent, which leads to the beliefs on discrediting women and rape myths. Specifically, we first propose a novel malicious intent typology for social media using the guidance of social construction theory from policy literature that includes Accusational, Validational, or Sensational intent categories. We then present and evaluate a malicious intent classification model for a Twitter post using semantic features of the intent senses learned with the help of convolutional neural networks. Lastly, we analyze a Twitter dataset of four months using the intent classification model to study narrative contexts in which malicious intents are expressed and discuss their implications for gender violence policy design.

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Intent Mining for the Good, Bad, and Ugly Use of Social Web: Concepts, Methods, and Challenges

Cited by 12 publications

References 41 publications

Developing smart city services using intent‐aware recommendation systems: A survey

Developing smart city services using intent‐aware recommendation systems: A survey

Challenges to Transforming Unconventional Social Media Data into Actionable Knowledge for Public Health Systems During Disasters

Distributional Semantics Approach to Detect Intent in Twitter Conversations on Sexual Assaults

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