Characterizing Social TV Activity Around Televised Events: A Joint Topic Model Approach

2021 ◽  
Author(s):  
Yuheng Hu
Keyword(s):  
Social Media ◽  
Topic Model ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
The State ◽  
State Of The Union ◽  
Social Tv ◽  
Social Media Platforms ◽  
Tweet Classification ◽  
Model Approach

Viewers often use social media platforms like Twitter to express their views about televised programs and events like the presidential debate, the Oscars, and the State of the Union speech. Although this promises tremendous opportunities to analyze the feedback on a program or an event using viewer-generated content on social media, there are significant technical challenges to doing so. Specifically, given a televised event and related tweets about this event, we need methods to effectively align these tweets and the corresponding event. In turn, this will raise many questions, such as how to segment the event and how to classify a tweet based on whether it is generally about the entire event or specifically about one particular event segment. In this paper, we propose and develop a novel joint Bayesian model that aligns an event and its related tweets based on the influence of the event’s topics. Our model allows the automated event segmentation and tweet classification concurrently. We present an efficient inference method for this model and a comprehensive evaluation of its effectiveness compared with the state-of-the-art methods. We find that the topics, segments, and alignment provided by our model are significantly more accurate and robust.

Towards an understanding of Twitter networks: The case of the state of Mexico

First Monday ◽  
2018 ◽  
Author(s):  
Rodrigo Sandoval-Almazán ◽  
David Valle-Cruz
Keyword(s):  
Social Media ◽  
The State ◽  
Decision Makers ◽  
Online Social Media ◽  
State Of The Union ◽  
Network Links ◽  
Social Media Platforms ◽  
The Impact ◽  
Networked Individualism ◽  
Government Promotion

The purpose of this research is to provide some understanding of Twitter Networks, using the data from the Twitter discussion generated on the State of the Union from Governor Eruviel Avila in 2015 and 2016. We analyzed and compared Network Links from tweets of two years, using Netlytic as a mining text tool. This research presents two contributions: 1) the links analysis perspective of social media; and, 2) we proposes a methodology to assess the impact of online social media and government promotion. Our findings suggest that citizens use social media platforms to interact with politicians in offices, and supports the argument about “networked individualism” in which analyzed Twitter accounts reveal many citizens’ opinions and retweets related to the governor’s use of YouTube for his State of the Union speech. Decision-makers can use this study to improve communication with their customers (public) and allocate resources effectively for better public services. Finally, the last trend has tried to understand content analysis by wording. There is a lack of research about network links, their quality and users that are part of such network to understand in an integrated perspective the impact of social media.

IT for Peace? Fighting Against Terrorism in Social Media – An Explorative Twitter Study

i-com ◽  
2017 ◽  
Vol 16 (2) ◽  
pp. 181-193 ◽  
Author(s):  
Christian Reuter ◽  
Katja Pätsch ◽  
Elena Runft
Keyword(s):  
Social Media ◽  
Information Systems ◽  
Empirical Study ◽  
State Of The Art ◽  
The State ◽  
The Internet ◽  
New Members ◽  
Starting Point ◽  
The Media

AbstractThe Internet and especially social media are not only used for supposedly good purposes. For example, the recruitment of new members and the dissemination of ideologies of terrorism also takes place in the media. However, the fight against terrorism also makes use of the same tools. The type of these countermeasures, as well as the methods, are covered in this work. In the first part, the state of the art is summarized. The second part presents an explorative empirical study of the fight against terrorism in social media, especially on Twitter. Different, preferably characteristic forms are structured within the scope with the example of Twitter. The aim of this work is to approach this highly relevant subject with the goal of peace, safety and safety from the perspective of information systems. Moreover, it should serve following researches in this field as basis and starting point.

Public Service Media and Social TV: Bridging or Expanding Gaps in Participation?

2015 ◽  
Vol 154 (1) ◽  
pp. 89-100 ◽  
Author(s):  
Jonathon Hutchinson
Keyword(s):  
Social Media ◽  
National Culture ◽  
Public Service ◽  
Cultural Production ◽  
The Public ◽  
Social Tv ◽  
Content Consumption ◽  
Social Media Platforms ◽  
Cultural Intermediaries ◽  
The Public Sphere

The public service media (PSM) remit requires the Australian Broadcasting Corporation (ABC) to provide for minorities while fostering national culture and the public sphere. Social media platforms and projects – specifically ‘social TV’ – have enabled greater participation in ABC content consumption and creation; they provide opportunities for social participation in collaborative cultural production. However it can be argued that, instead of deconstructing boundaries, social media platforms may in fact reconstruct participation barriers within PSM production processes. This article explores ABC co-creation between Twitter and the # 7DaysLater television program, a narrative-based comedy program that engaged its audience through social media to produce its weekly program. The article demonstrates why the ABC should engage with social media platforms to collaboratively produce content, with # 7DaysLater providing an innovative example, but suggests skilled cultural intermediaries with experience in community facilitation should carry out the process.

scholarly journals News Organizations’ Selective Link Sharing as Gatekeeping : A Structural Topic Model Approach

2019 ◽  
Vol 1 (1) ◽  
pp. 45-78
Author(s):  
Chankyung Pak
Keyword(s):  
Social Media ◽  
Topic Model ◽  
News Stories ◽  
News Websites ◽  
News Organizations ◽  
Computational Data ◽  
Topic Distribution ◽  
Editorial Decisions ◽  
Structural Topic Model ◽  
Model Approach

Abstract To disseminate their stories efficiently via social media, news organizations make decisions that resemble traditional editorial decisions. However, the decisions for social media may deviate from traditional ones because they are often made outside the newsroom and guided by audience metrics. This study focuses on selective link sharing as quasi-gatekeeping on Twitter ‐ conditioning a link sharing decision about news content. It illustrates how selective link sharing resembles and deviates from gatekeeping for the publication of news stories. Using a computational data collection method and a machine learning technique called Structural Topic Model (STM), this study shows that selective link sharing generates a different topic distribution between news websites and Twitter and thus significantly revokes the specialty of news organizations. This finding implies that emergent logic, which governs news organizations’ decisions for social media, can undermine the provision of diverse news.

Transitive Topic Modeling with Conversational Structure Context: Discovering Topics that are Most Popular in Online Discussions

2020 ◽  
Vol 14 (02) ◽  
pp. 273-293
Author(s):  
Yingcheng Sun ◽  
Richard Kolacinski ◽  
Kenneth Loparo
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
Topic Model ◽  
Online Discussions ◽  
Challenging Problem ◽  
Topic Extraction ◽  
Limited Success ◽  
Social Media Platforms ◽  
Improved Performance ◽  
Conversational Structure

With the explosive growth of online discussions published everyday on social media platforms, comprehension and discovery of the most popular topics have become a challenging problem. Conventional topic models have had limited success in online discussions because the corpus is extremely sparse and noisy. To overcome their limitations, we use the discussion thread tree structure and propose a “popularity” metric to quantify the number of replies to a comment to extend the frequency of word occurrences, and the “transitivity” concept to characterize topic dependency among nodes in a nested discussion thread. We build a Conversational Structure Aware Topic Model (CSATM) based on popularity and transitivity to infer topics and their assignments to comments. Experiments on real forum datasets are used to demonstrate improved performance for topic extraction with six different measurements of coherence and impressive accuracy for topic assignments.

scholarly journals Towards Combating Pandemic-Related Misinformation in Social Media

2021 ◽  
pp. 140-158
Author(s):  
Isa Inuwa-Dutse
Keyword(s):  
Social Media ◽  
Public Discourse ◽  
Preventive Measures ◽  
State Of The Art ◽  
Fake News ◽  
Social Distancing ◽  
The Public ◽  
Toxic Impact ◽  
Online Social Media ◽  
Social Media Platforms

Conventional preventive measures during pandemics include social distancing and lockdown. Such measures in the time of social media brought about a new set of challenges – vulnerability to the toxic impact of online misinformation is high. A case in point is COVID-19. As the virus propagates, so does the associated misinformation and fake news about it leading to an infodemic. Since the outbreak, there has been a surge of studies investigating various aspects of the pandemic. Of interest to this chapter are studies centering on datasets from online social media platforms where the bulk of the public discourse happens. The main goal is to support the fight against negative infodemic by (1) contributing a diverse set of curated relevant datasets; (2) offering relevant areas to study using the datasets; and (3) demonstrating how relevant datasets, strategies, and state-of-the-art IT tools can be leveraged in managing the pandemic.

scholarly journals #Healthy Selfies: Exploration of Health Topics on Instagram (Preprint)

2018 ◽  
Author(s):  
Sachin Muralidhara ◽  
Michael J. Paul
Keyword(s):  
Public Health ◽  
Social Media ◽  
Substance Use ◽  
Topic Model ◽  
Public Health Research ◽  
Natural Languages ◽  
Image Sharing ◽  
Public Data ◽  
Public Health Information ◽  
Social Media Platforms

BACKGROUND Social media provides a complementary source of information for public health surveillance. The dominate data source for this type of monitoring is the microblogging platform Twitter, which is convenient due to the free availability of public data. Less is known about the utility of other social media platforms, despite their popularity. OBJECTIVE This work aims to characterize the health topics that are prominently discussed in the image-sharing platform Instagram, as a step toward understanding how this data might be used for public health research. METHODS The study uses a topic modeling approach to discover topics in a dataset of 96,426 Instagram posts containing hashtags related to health. We use a polylingual topic model, initially developed for datasets in different natural languages, to model different modalities of data: hashtags, caption words, and image tags automatically extracted using a computer vision tool. RESULTS We identified 47 health-related topics in the data (kappa=.77), covering ten broad categories: acute illness, alternative medicine, chronic illness and pain, diet, exercise, health care & medicine, mental health, musculoskeletal health and dermatology, sleep, and substance use. The most prevalent topics were related to diet (8,293/96,426; 8.6% of posts) and exercise (7,328/96,426; 7.6% of posts). CONCLUSIONS A large and diverse set of health topics are discussed in Instagram. The extracted image tags were generally too coarse and noisy to be used for identifying posts but were in some cases accurate for identifying images relevant to studying diet and substance use. Instagram shows potential as a source of public health information, though limitations in data collection and metadata availability may limit its use in comparison to platforms like Twitter.

scholarly journals GIF Video Sentiment Detection Using Semantic Sequence

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Dazhen Lin ◽  
Donglin Cao ◽  
Yanping Lv ◽  
Zheng Cai
Keyword(s):  
Social Media ◽  
State Of The Art ◽  
The State ◽  
Middle Level ◽  
Semantic Gap ◽  
Classification Methods ◽  
Detection Approach ◽  
Semantic Concepts ◽  
Sentiment Detection ◽  
Media Applications

With the development of social media, an increasing number of people use short videos in social media applications to express their opinions and sentiments. However, sentiment detection of short videos is a very challenging task because of the semantic gap problem and sequence based sentiment understanding problem. In this context, we propose a SentiPair Sequence based GIF video sentiment detection approach with two contributions. First, we propose a Synset Forest method to extract sentiment related semantic concepts from WordNet to build a robust SentiPair label set. This approach considers the semantic gap between label words and selects a robust label subset which is related to sentiment. Secondly, we propose a SentiPair Sequence based GIF video sentiment detection approach that learns the semantic sequence to understand the sentiment from GIF videos. Our experiment results on GSO-2016 (GIF Sentiment Ontology) data show that our approach not only outperforms four state-of-the-art classification methods but also shows better performance than the state-of-the-art middle level sentiment ontology features, Adjective Noun Pairs (ANPs).

The State of Social Media in Saudi Arabia’s Higher Education

2013 ◽  
Vol 3 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Hend S. Al-Khalifa ◽  
Regina A. Garcia
Keyword(s):  
Higher Education ◽  
Social Media ◽  
Saudi Arabia ◽  
Teaching And Learning ◽  
Higher Education Institutions ◽  
The State ◽  
Media In Education ◽  
Universities And Colleges ◽  
Exchange Of Information ◽  
Social Media Platforms

Social media platforms are designed not only for entertainment but also for exchange of information, collaboration, teaching and learning. With this, Higher Education institutions in Saudi Arabia have started utilizing these platforms for the main reason that many students are embracing this new trend in technology. In this article, a discussion of this media in education in terms of its roles, used in different settings, and its policies and management in accordance with Saudi culture will be covered. Furthermore, the state of this media in Higher Education institutions among the country’s universities and colleges will be highlighted.

Contesting the Vulgar Hanmai Performance from Kuaishou

2020 ◽  
pp. 49-76
Author(s):  
Jiaxi Hou
Keyword(s):  
Social Media ◽  
The State ◽  
Role Of The State ◽  
Social Media Platforms

Jiaxi Hou’s chapter considers the prominence and scrutiny of an underclass subculture in China, and how its visibility led to denunciations by other communities. Here, the mediated visibility brought about by social media platforms serves both to affirm a community of supporters as well as to incite scrutiny and rebuke from a broader audience. In considering the role of the state and platform in the context of broader socio-cultural circumstances, this chapter presents vigilant audiences as shaped by a multiplicity of actors. Even a denunciatory label like ‘vulgarity’ can be unpacked to refer to a range of offences, targeting not just individual artists but a broader social underclass in the name of collective morality.

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