Filtering microblogging messages for social tv

Dan, Ovidiu; Feng, Jianhua; Davison, Brian D.

doi:10.1145/1963192.1963288

Cited by 9 publications

(5 citation statements)

References 10 publications

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“…More broadly, understanding how tv program viewers process TV program and how their mood state affects their behavior is fundamentally important and have implications for a wide range of areas, including journalism, e.g., event sentiment analysis (Dan, Feng, and Davison 2011;Hu, Wang, and Kambhampati 2013) and communication (Knobloch 2003). For example, in marketing, brands needs to better understand the way consumers encode information from the ad and, to what extent, their ad-viewing experience is influenced by the mood states induced by the program.…”

Section: Discussion and Implicationmentioning

confidence: 99%

Mood Congruence or Mood Consistency? Examining Aggregated Twitter Sentiment Towards Ads in 2016 Super Bowl

Nian

Chen

2017

ICWSM

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While the popularity of Social TV has recently attracted significant research efforts from various research communities, little is known about how viewers' Twitter sentiment toward TV advertising is affected by the viewers' mood state affected by the TV program. In this paper, we take a large set of tweets posted during the Super Bowl 2016 to investigate the effect of audience's mood induced by the game on their reactions the Super Bowl commercials. Our results find the support for the mood congruence theory, suggesting that game-induced mood has a positive and significant effect on the viewers' Twitter sentiment towards the commercials. We also discuss both theoretical and practical implications for our study.

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Section: Discussion and Implicationmentioning

confidence: 99%

Mood Congruence or Mood Consistency? Examining Aggregated Twitter Sentiment Towards Ads in 2016 Super Bowl

Nian

Chen

2017

ICWSM

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“…Several automatic variants of social curation have also been developed, such as Paper.li 4) , Gunosy 5) and Primal 6) , which automatically select news articles, 1) http://pinterest.com 2) http://storify.com 3) https://dev.twitter.com/docs/custom-timelines 4) http://paper.li 5) http://gunosy.com 6) http://wordpress.org/plugins/primal-for-wp/ blogs, micro messages, photos and videos that are highly relevant to users' interests and social activities. "Matome": Unique curation culture in Japan Social curation services have been well established in Japan for these 10 years, which Japanese usually calls "Matome".…”

Section: Automatic Alternatives Of Manual Social Curationmentioning

confidence: 99%

“…Following this trend, the development of new algorithms for social media analysis is now one of the most active research topics. Topic detection [7], [17], tracking [29] and summarization [2], [3], [28], and automatic filtering [5], [32], [39] in microblogs would be typical tasks in this field. In addition, the major interest of the current SNS devel-opers and users are rapidly shifting from conventional text-based (micro)blogs such as Twitter and Facebook to multimedia contents such as Flickr, Snapchat, MySpace and Tumblr.…”

Section: Introductionmentioning

confidence: 99%

Large-scale cross-media analysis and mining from socially curated contents

Kimura¹

2014

Prog. Inform.

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The major interest of the current social network service (SNS) developers and users are rapidly shifting from conventional text-based (micro)blogs such as Twitter and Facebook to multimedia contents such as Flickr, Snapchat, MySpace and Tumblr. However, the ability to analyze and exploit unorganized multimedia contents on those services still remain inadequate, even with state-of-the-art media processing and machine learning techniques. This paper focuses on another emerging trend called social curation, a human-in-the-loop alternative to automatic algorithms for social media analysis. Social curation can be defined as a spontaneous human process of remixing social media content for the purpose of further consumption. What characterize social curation are definitely the manual efforts involved in organizing a collection of social media contents, which indicates that socially curated content has a potential as a promising information source against automatic summaries generated by algorithms. Curated contents would also provide latent perspectives and contexts that are not explicitly presented in the original resources. Following this trend, this paper presents recent developments and growth of social curation services, and reviews several research trials for cross-media analysis and mining from socially curated contents.

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“…In principle, social media research involving user recommendation [12]- [14], microblog filtering [5], [6], topic modeling [15], [16], event summarization [3], [4], and activity stream personalization [17] may all be beneficial for assisting curators. Microblog ranking and recommendation based on content freshness [18] and account authority [19] would be promising for finding seeds of stories that attract much attention of consumers.…”

Section: Related Workmentioning

confidence: 99%

“…As such, an active research area is the development of new algorithms for social media analysis. Examples include topic detection [2], event summarization [3], [4], and automatic filtering [5], [6] in microblogs. We imagine these algorithmic advances will provide efficient ways to discover and summarize events of interest from large streams of social media.…”

Section: Introductionmentioning

confidence: 99%

Creating Stories from Socially Curated Microblog Messages

Kimura

Duh

Hirao

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYSocial media such as microblogs have become so pervasive such that it is now possible to use them as sensors for real-world events and memes. While much recent research has focused on developing automatic methods for filtering and summarizing these data streams, we explore a different trend called social curation. In contrast to automatic methods, social curation is characterized as a human-in-the-loop and sometimes crowd-sourced mechanism for exploiting social media as sensors. Although social curation web services like Togetter, Naver Matome and Storify are gaining popularity, little academic research has studied the phenomenon. In this paper, our goal is to investigate the phenomenon and potential of this new field of social curation. First, we perform an in-depth analysis of a large corpus of curated microblog data. We seek to understand why and how people participate in this laborious curation process. We then explore new ways in which information retrieval and machine learning technologies can be used to assist curators. In particular, we propose a novel method based on a learning-to-rank framework that increases the curator's productivity and breadth of perspective by suggesting which novel microblogs should be added to the curated content.

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Filtering microblogging messages for social tv

Cited by 9 publications

References 10 publications

Mood Congruence or Mood Consistency? Examining Aggregated Twitter Sentiment Towards Ads in 2016 Super Bowl

Mood Congruence or Mood Consistency? Examining Aggregated Twitter Sentiment Towards Ads in 2016 Super Bowl

Large-scale cross-media analysis and mining from socially curated contents

Creating Stories from Socially Curated Microblog Messages

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