scholarly journals Extracting Emergent Semantics from Large-Scale User-Generated Content

2012 ◽  
pp. 27-37
Author(s):  
Ioannis Kompatsiaris ◽  
Sotiris Diplaris ◽  
Symeon Papadopoulos
Keyword(s):  
Large Scale ◽  
User Generated Content ◽  
Emergent Semantics

scholarly journals Harsh Words and Deeds: Systematic Content Analyses of Offensive* User Behavior in the Virtual Environments of Online First-Person Shooter Games

2017 ◽  
Vol 10 (2) ◽  
Author(s):  
Adrienne Holz Ivory ◽  
James D. Ivory ◽  
Winston Wu ◽  
Anthony M. Limperos ◽  
Nathaniel Andrew ◽  
...  
Keyword(s):  
Virtual Environments ◽  
Video Game ◽  
Large Scale ◽  
Online Games ◽  
User Generated Content ◽  
First Person ◽  
Online Game ◽  
Full Spectrum ◽  
Content Analyses ◽  
First Person Shooter

While the virtual environments of online games can foster healthy relationships and strong communities, some online games are also marred by antisocial and offensive behavior. Such behavior, even when relatively rare, influences the interactions and relationships of users in online communities. Thus, understanding the prevalence and nature of antisocial and offensive behaviors in online games is an important step toward understanding the full spectrum of healthy and unhealthy interactions and relationships in virtual environments. Extensive research has explored video game content produced by game developers, such as violence, profanity, and sexualized portrayals, but much less research has systematically examined potentially problematic content produced by players in online games. While potential effects of antisocial and offensive online game content are not well understood, a first step toward exploring this concern is systematic documentation of offensive user-generated content in online games. To that end, two large-scale content analyses measured a range of offensive user-generated content, including utterances, text, and images, from a total of more than 2,500 users in popular first-person shooter video games. Findings indicated that some content, such as profanity, was frequent among users who spoke during games. More offensive and potentially harmful content, such as racial slurs, was proportionally very rare, but frequent enough to be encountered often by regular players. Results of this initial investigation should be interpreted tentatively, do not suggest that relationships in online shooter games lack healthy elements, and should not be generalized to other online game communities until further research is conducted.* Note: This paper contains strong language which may be offensive to some readers.

Reading between the lines: untwining online user-generated content using sentiment analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Gowhar Rasool ◽  
Anjali Pathania
Keyword(s):  
Service Quality ◽  
Sentiment Analysis ◽  
Airline Industry ◽  
Large Scale ◽  
Low Cost ◽  
Marketing Research ◽  
Analytical Framework ◽  
User Generated Content ◽  
Knowledge Domain ◽  
Content Type

PurposeOne of the major challenges within the airline industry is to keep pace with the changing customer perception toward their service quality. This paper aims to demonstrate how sentiment analysis of user-generated big data can be used to research airline service quality as a more comprehensive alternative to other survey-based models by investigating real-time passenger insights.Design/methodology/approachThe present research uses the case of Indigo airlines by studying passenger's trip advisor reviews regarding the low-cost commercial airline service. The authors analyzed 1,777 passenger reviews, which were classified, to uncover sentiments for five dimensions of airline service quality (AIRQUAL).FindingsThe findings of the study demonstrate the need for harnessing the brand-related user-generated content shared on online platforms to identify the critical attributes for airline service quality. Further, through the application of sentiment analysis, the paper provides much-needed clarity in the processing of user-generated content. It illustrates the investigation of passenger interactions as a reflection of their satisfaction, expectation, intention and overall opinion toward the airline service quality.Practical implicationsThe analytical framework adopted in the study for examining user-generated content (UGC) can be functional for the marketing managers and equip them for handling large-scale data readily available in action-oriented interactive marketing research.Originality/valueThis paper demonstrates how sentiment analysis of user-generated data can be used to research airline service quality as a more comprehensive alternative to other survey-based models. The study supplements the methodological advances in the field of UGC analysis and adds to the existing knowledge domain.

PUGCQ: A Large Scale Dataset for Quality Assessment of Professional User-Generated Content

2021 ◽  
Author(s):  
Guo Li ◽  
Baoliang Chen ◽  
Lingyu Zhu ◽  
Qinwen He ◽  
Hongfei Fan ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Large Scale ◽  
User Generated Content ◽  
Large Scale Dataset

scholarly journals Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems

2009 ◽  
Vol 17 (5) ◽  
pp. 1357-1370 ◽  
Author(s):  
Meeyoung Cha ◽  
Haewoon Kwak ◽  
P. Rodriguez ◽  
Yong-Yeol Ahn ◽  
Sue Moon
Keyword(s):  
Large Scale ◽  
User Generated Content

scholarly journals Algorithmic content moderation: Technical and political challenges in the automation of platform governance

2020 ◽  
Author(s):  
Robert Gorwa ◽  
Reuben Binns ◽  
Christian Katzenbach
Keyword(s):  
Large Scale ◽  
Hate Speech ◽  
Ethical Issues ◽  
User Generated Content ◽  
Learning Tools ◽  
Existing Problems ◽  
Platform Governance ◽  
Public Expectations ◽  
Automated Tools ◽  
Technical Solutions

As government pressure on major technology companies builds, both firms and legislators are searching for technical solutions to difficult platform governance puzzles such as hate speech and misinformation. Automated hash-matching and predictive machine learning tools – what we define here as algorithmic moderation systems – are increasingly being deployed to conduct content moderation at scale by major platforms for user-generated content such as Facebook, YouTube and Twitter. This article provides an accessible technical primer on how algorithmic moderation works; examines some of the existing automated tools used by major platforms to handle copyright infringement, terrorism and toxic speech; and identifies key political and ethical issues for these systems as the reliance on them grows. Recent events suggest that algorithmic moderation has become necessary to manage growing public expectations for increased platform responsibility, safety and security on the global stage; however, as we demonstrate, these systems remain opaque, unaccountable and poorly understood. Despite the potential promise of algorithms or ‘AI’, we show that even ‘well optimized’ moderation systems could exacerbate, rather than relieve, many existing problems with content policy as enacted by platforms for three main reasons: automated moderation threatens to (a) further increase opacity, making a famously non-transparent set of practices even more difficult to understand or audit, (b) further complicate outstand- ing issues of fairness and justice in large-scale sociotechnical systems and (c) re-obscure the fundamentally political nature of speech decisions being executed at scale.

scholarly journals Implicit Feedback Recommendation Method Based on User-Generated Content

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bing Fang ◽  
Enpeng Hu ◽  
Junyang Shen ◽  
Jingwen Zhang ◽  
Yang Chen
Keyword(s):  
Large Scale ◽  
Semantic Analysis ◽  
User Preferences ◽  
User Preference ◽  
User Generated Content ◽  
Implicit Feedback ◽  
Rating Data ◽  
Customer Needs ◽  
Sparsity Problem ◽  
Customer Need

Studying recommendation method has long been a fundamental area in personalized marketing science. The rating data sparsity problem is the biggest challenge of recommendations. In addition, existing recommendation methods can only identify user preferences rather than customer needs. To solve these two bottleneck problems, we propose a novel implicit feedback recommendation method using user-generated content (UGC). We identify product feature and customer needs from UGC using Convolutional Neural Network (CNN) model and textual semantic analysis techniques, measure user-product fit degree introducing attention mechanism and antonym mechanism, and predict user rating based on user-product fit degree and user history rating data. Using data from a large-scale review sites, we demonstrate the effectiveness of our proposed method. Our study makes several research contributions. First, we propose a novel recommendation method with strong robustness against sparse rating data. Second, we propose a novel recommendation method based on the customer need-product feature fit. Third, we propose a novel approach to measure the fit degree of customer needs-product feature, which can effectively improve the performance of recommendation method. Our study also indicates the following findings: (1) UGC can be used to predict user ratings with no user rating records. This finding has important implications to solve the sparsity problem of recommendations thoroughly. (2) The customer need-based recommendation method has better performance than existing user preference-based recommendation methods. This finding sheds light on the necessity of mining customer need for recommendation methods. (3) UGC can be used to mine customer need and product features. This finding indicates that UGC also can be used in the other studies requiring information about customer need and product feature. (4) Comparing the opinions of user review should not be solely on the basis of semantic similarity. This finding sheds light on the limitation of existing opinion mining studies.

Harsh Words and Deeds: Systematic Content Analyses of Offensive User Behavior in the Virtual Environments of Online First-Person Shooter Games

2016 ◽  
Vol 10 (2) ◽  
Author(s):  
Adrienne Holz Ivory ◽  
James D. Ivory ◽  
Winston Wu ◽  
Anthony M. Limperos ◽  
Nathaniel Andrew ◽  
...  
Keyword(s):  
Video Game ◽  
Large Scale ◽  
User Behavior ◽  
Online Games ◽  
Systematic Investigation ◽  
User Generated Content ◽  
First Person ◽  
Online Game ◽  
Content Analyses ◽  
First Person Shooter

Extensive research has examined the prevalence and potential effects of potentially harmful video game content produced by game developers, such as violence, profanity, and sexualized portrayals, but much less research has systematically examined the large range of potentially problematic content produced by players in increasingly popular online games. This player-generated content may actually be of more social concern than content programmed in the games, as it is largely undocumented and unaddressed by industry ratings and consumer advisory groups. While potential effects of such antisocial and offensive online game content are not well understood, a first step toward exploring this concern is systematic documentation of offensive user-generated content in online games. To that end, a pair of large-scale systematic content analyses documented a range of offensive user-generated content, including utterances, text, and images, from a total of more than 2,500 users in popular first-person shooter video games. Findings indicated that some content, such as profanity, were frequent among users who spoke during games. More offensive and potentially harmful content such as racial slurs was proportionally very rare but frequent enough to be encountered often by regular game players. Implications for further research and practice are discussed. Results should be interpreted tentatively based on this relatively unprecedented systematic investigation, should not be interpreted as evidence that online shooter games are harmful or lack healthy elements, and should not be extrapolated to other online game genres and communities until further research is conducted.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Evolution of Large-Scale Coronal Structures

1994 ◽  
Vol 144 ◽  
pp. 29-33
Author(s):  
P. Ambrož
Keyword(s):  
Magnetic Field ◽  
Field Line ◽  
Large Scale ◽  
Coronal Magnetic Field ◽  
Source Surface ◽  
Solar Cycles ◽  
Characteristic Relationship ◽  
The Magnetic Field ◽  
Coronal Structures

AbstractThe large-scale coronal structures observed during the sporadically visible solar eclipses were compared with the numerically extrapolated field-line structures of coronal magnetic field. A characteristic relationship between the observed structures of coronal plasma and the magnetic field line configurations was determined. The long-term evolution of large scale coronal structures inferred from photospheric magnetic observations in the course of 11- and 22-year solar cycles is described.Some known parameters, such as the source surface radius, or coronal rotation rate are discussed and actually interpreted. A relation between the large-scale photospheric magnetic field evolution and the coronal structure rearrangement is demonstrated.

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