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.

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

2019 ◽  
Author(s):  
Chankyung Pak
Keyword(s):  
Social Media ◽  
Topic Model ◽  
News Websites ◽  
News Organizations ◽  
Computational Data ◽  
Learning Technique ◽  
Topic Distribution ◽  
Editorial Decisions ◽  
Structural Topic Model ◽  
Model Approach

To disseminate their stories efficiently via social media, news organizations make decisionsthat resemble traditional editorial decisions. However, the decisions for social media maydeviate from traditional ones because they are often made outside the newsroom and guidedby audience metrics. This study focuses on selective link sharing as quasi-gatekeeping onTwitter – conditioning a link sharing decision about news content. It illustrates how selectivelink sharing resembles and deviates from gatekeeping for the publication of news stories.Using a computational data collection method and a machine learning technique calledStructural Topic Model (STM), this study shows that selective link sharing generates adifferent topic distribution between news websites and Twitter and thus significantly revokesthe specialty of news organizations. This finding implies that emergent logic, which governsnews organizations’ decisions for social media can undermine the provision of diverse news,which relies on journalistic values and norms.

scholarly journals Trends in the diffusion of misinformation on social media

2019 ◽  
Vol 6 (2) ◽  
pp. 205316801984855 ◽  
Author(s):  
Hunt Allcott ◽  
Matthew Gentzkow ◽  
Chuan Yu
Keyword(s):  
Social Media ◽  
Relative Magnitude ◽  
Democratic Institutions ◽  
Fake News ◽  
User Interactions ◽  
News Stories ◽  
News Websites ◽  
Social Media Platforms

In recent years, there has been widespread concern that misinformation on social media is damaging societies and democratic institutions. In response, social media platforms have announced actions to limit the spread of false content. We measure trends in the diffusion of content from 569 fake news websites and 9540 fake news stories on Facebook and Twitter between January 2015 and July 2018. User interactions with false content rose steadily on both Facebook and Twitter through the end of 2016. Since then, however, interactions with false content have fallen sharply on Facebook while continuing to rise on Twitter, with the ratio of Facebook engagements to Twitter shares decreasing by 60%. In comparison, interactions with other news, business, or culture sites have followed similar trends on both platforms. Our results suggest that the relative magnitude of the misinformation problem on Facebook has declined since its peak.

Organizational adaptations to social media: How social media news workers in the Philippines are embedded in newsrooms and influences on editorial practices

2019 ◽  
Vol 40 (3) ◽  
pp. 329-345 ◽  
Author(s):  
Clarissa C. David ◽  
Edson C. Tandoc ◽  
Evelyn Katigbak
Keyword(s):  
Social Media ◽  
Market Orientation ◽  
Television News ◽  
The Philippines ◽  
Mass Market ◽  
Direct Exposure ◽  
News Organizations ◽  
History Of ◽  
Editorial Decisions ◽  
Editorial Practices

Through interviews with journalists from four top online newsrooms in the Philippines, this study examined the organizational arrangements surrounding social media teams and how these influence social media being incorporated into journalism decisions. Organizations considered audience preferences in their editorial decisions, but they depended on arrangements surrounding social media teams. Some organizational arrangements included inclusion of social media editors in story conferences and meetings, collaboration between reporters and social media teams, and direct exposure of top editors to engagement analytics. Drivers of news organizations incorporating social media into newsmaking processes include mass-market orientation, primacy of digital over print/television news formats, and history of a legacy brand.

scholarly journals Joint Topical Word Embedding for Detecting Drift in Social Media Text

2020 ◽  
Author(s):  
VIJAYARANI J ◽  
Geetha T.V.
Keyword(s):  
Social Media ◽  
Topic Model ◽  
Rate Of Change ◽  
Word Embedding ◽  
Human Interaction ◽  
Langevin Dynamic ◽  
Context Vector ◽  
The Social ◽  
Topic Distribution ◽  
Social Media Text

Abstract Social media texts like tweets and blogs are collaboratively created by human interaction. Fast change in trends leads to topic drift in the social media text. This drift is usually associated with words and hashtags. However, geotags play an important part in determining topic distribution with location context. Rate of change in the distribution of words, hashtags and geotags cannot be considered as uniform and must be handled accordingly. This paper builds a topic model that associates topic with a mixture of distributions of words, hashtags and geotags. Stochastic gradient Langevin dynamic model with varying mini-batch sizes is used to capture the changes due to the asynchronous distribution of words and tags. Topical word embedding with co-occurrence and location contexts are specified as hashtag context vector and geotag context vector respectively. These two vectors are jointly learned to yield topical word embedding vectors related to tags context. Topical word embeddings over time conditioned on hashtags and geotags predict, location-based topical variations effectively. When evaluated with Chennai and UK geolocated Twitter data, the proposed joint topical word embedding model enhanced by the social tags context, outperforms other methods.

scholarly journals Joint Topical Word Embedding for Detecting Drift in Social Media Text

2020 ◽  
Author(s):  
VIJAYARANI J ◽  
Geetha T.V.
Keyword(s):  
Social Media ◽  
Topic Model ◽  
Rate Of Change ◽  
Word Embedding ◽  
Human Interaction ◽  
Langevin Dynamic ◽  
Context Vector ◽  
The Social ◽  
Topic Distribution ◽  
Social Media Text

Abstract Social media texts like tweets and blogs are collaboratively created by human interaction. Fast change in trends leads to topic drift in the social media text. This drift is usually associated with words and hashtags. However, geotags play an important part in determining topic distribution with location context. Rate of change in the distribution of words, hashtags and geotags cannot be considered as uniform and must be handled accordingly. This paper builds a topic model that associates topic with a mixture of distributions of words, hashtags and geotags. Stochastic gradient Langevin dynamic model with varying mini-batch sizes is used to capture the changes due to the asynchronous distribution of words and tags. Topical word embedding with co-occurrence and location contexts are specified as hashtag context vector and geotag context vector respectively. These two vectors are jointly learned to yield topical word embedding vectors related to tags context. Topical word embeddings over time conditioned on hashtags and geotags predict, location-based topical variations effectively. When evaluated with Chennai and UK geolocated Twitter data, the proposed joint topical word embedding model enhanced by the social tags context, outperforms other methods.

scholarly journals Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach (Preprint)

2017 ◽  
Author(s):  
Redhouane Abdellaoui ◽  
Pierre Foulquié ◽  
Nathalie Texier ◽  
Carole Faviez ◽  
Anita Burgun ◽  
...  
Keyword(s):  
Social Media ◽  
Virtual Communities ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Antidepressant Drug ◽  
Topic Models ◽  
Web Crawler ◽  
Probabilistic Topic Model ◽  
Manual Review ◽  
Model Approach

BACKGROUND Medication nonadherence is a major impediment to the management of many health conditions. A better understanding of the factors underlying noncompliance to treatment may help health professionals to address it. Patients use peer-to-peer virtual communities and social media to share their experiences regarding their treatments and diseases. Using topic models makes it possible to model themes present in a collection of posts, thus to identify cases of noncompliance. OBJECTIVE The aim of this study was to detect messages describing patients’ noncompliant behaviors associated with a drug of interest. Thus, the objective was the clustering of posts featuring a homogeneous vocabulary related to nonadherent attitudes. METHODS We focused on escitalopram and aripiprazole used to treat depression and psychotic conditions, respectively. We implemented a probabilistic topic model to identify the topics that occurred in a corpus of messages mentioning these drugs, posted from 2004 to 2013 on three of the most popular French forums. Data were collected using a Web crawler designed by Kappa Santé as part of the Detec’t project to analyze social media for drug safety. Several topics were related to noncompliance to treatment. RESULTS Starting from a corpus of 3650 posts related to an antidepressant drug (escitalopram) and 2164 posts related to an antipsychotic drug (aripiprazole), the use of latent Dirichlet allocation allowed us to model several themes, including interruptions of treatment and changes in dosage. The topic model approach detected cases of noncompliance behaviors with a recall of 98.5% (272/276) and a precision of 32.6% (272/844). CONCLUSIONS Topic models enabled us to explore patients’ discussions on community websites and to identify posts related with noncompliant behaviors. After a manual review of the messages in the noncompliance topics, we found that noncompliance to treatment was present in 6.17% (276/4469) of the posts.

scholarly journals Who Shares and Comments on News?: A Cross-National Comparative Analysis of Online and Social Media Participation

2017 ◽  
Vol 3 (4) ◽  
pp. 205630511773575 ◽  
Author(s):  
Antonis Kalogeropoulos ◽  
Samuel Negredo ◽  
Ike Picone ◽  
Rasmus Kleis Nielsen
Keyword(s):  
Social Media ◽  
Comparative Analysis ◽  
Digital Media ◽  
Online News ◽  
Media Technologies ◽  
News Stories ◽  
High Interest ◽  
News Websites ◽  
Social Media Platforms ◽  
Cross National

In this article, we present a cross-national comparative analysis of which online news users in practice engage with the participatory potential for sharing and commenting on news afforded by interactive features in news websites and social media technologies across a strategic sample of six different countries. Based on data from the 2016 Reuters Institute Digital News Report, and controlling for a range of factors, we find that (1) people who use social media for news and a high number of different social media platforms are more likely to also engage more actively with news outside social media by commenting on news sites and sharing news via email, (2) political partisans on both sides are more likely to engage in sharing and commenting particularly on news stories in social media, and (3) people with high interest in hard news are more likely to comment on news on both news sites and social media and share stores via social media (and people with high interest in any kind of news [hard or soft] are more likely to share stories via email). Our analysis suggests that the online environment reinforces some long-standing inequalities in participation while countering other long-standing inequalities. The findings indicate a self-reinforcing positive spiral where the already motivated are more likely in practice to engage with the potential for participation offered by digital media, and a negative spiral where those who are less engaged participate less.

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.

scholarly journals Joint Topical Word Embedding for Detecting Drift in Social Media Text

2020 ◽  
Author(s):  
VIJAYARANI J ◽  
Geetha T.V.
Keyword(s):  
Social Media ◽  
Topic Model ◽  
Rate Of Change ◽  
Word Embedding ◽  
Human Interaction ◽  
Langevin Dynamic ◽  
Context Vector ◽  
The Social ◽  
Topic Distribution ◽  
Social Media Text

Abstract Social media texts like tweets and blogs are collaboratively created by human interaction. Fast change in trends leads to topic drift in the social media text. This drift is usually associated with words and hashtags. However, geotags play an important part in determining topic distribution with location context. Rate of change in the distribution of words, hashtags and geotags cannot be considered as uniform and must be handled accordingly. This paper builds a topic model that associates topic with a mixture of distributions of words, hashtags and geotags. Stochastic gradient Langevin dynamic model with varying mini-batch sizes is used to capture the changes due to the asynchronous distribution of words and tags. Topical word embedding with co-occurrence and location contexts are specified as hashtag context vector and geotag context vector respectively. These two vectors are jointly learned to yield topical word embedding vectors related to tags context. Topical word embeddings over time conditioned on hashtags and geotags predict, location-based topical variations effectively. When evaluated with Chennai and UK geolocated Twitter data, the proposed joint topical word embedding model enhanced by the social tags context, outperforms other methods.

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