Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter

Wischnewski, Magdalena; Bruns, Axel; Keller, Tobias

doi:10.1080/21670811.2021.1903960

Cited by 14 publications

(8 citation statements)

References 44 publications

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“…Recently, many studies have investigated the factors that influence mobile social media user information sharing behaviour. Regarding news information sharing, Wischnewski et al (2021) also demonstrated that hyperpartisan news stories are more likely to be shared by Twitter users if these stories match users' political views. In terms of video information sharing, Shehu et al (2016) pointed out that a user's preference for the beginning and end of the video can significantly affect his or her willingness to share the video online.…”

Section: Information Sharing Behavior On Mobile Social Mediamentioning

confidence: 99%

Medical Crowdfunding Campaign Sharing Behaviour on Mobile Social Media

Zhou

Yao

et al. 2022

Journal of Organizational and End User Computing

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This study explores factors influencing the sharing behaviour of medical crowdfunding campaigns on mobile social media regarding retweeter and retweeted text features from the social influence perspective and verifies the moderating effect of potential supporter donations. Two scenario-based surveys of medical crowdfunding users are conducted in China. The first (524 samples) and second (510 samples) surveys emphasize scenarios without and with retweeted text, respectively. These surveys are tested by PLS-SEM and suggest that the credibility of retweeters and the strength of their relationship with supporters can positively promote potential supporters' sharing behaviour, regardless of whether retweeters provide retweeted text. Three features of retweeted text significantly influence medical crowdfunding sharing behaviour. Retweeted text can promote sharing behaviour among individuals. With retweeted text available, if potential supporters donate before sharing the campaign, then the influences of retweeter and retweeted text features can be increased.

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Section: Information Sharing Behavior On Mobile Social Mediamentioning

confidence: 99%

Medical Crowdfunding Campaign Sharing Behaviour on Mobile Social Media

Zhou

Yao

et al. 2022

Journal of Organizational and End User Computing

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“…One area of relevant research considers the factors contributing to engagement with hyperpartisan news, misinformation, and conspiracy theories. In some cases, the content is highlighted, such as its topic and tone (Wischnewski, Bruns, and Keller 2021), as well as how such content aligns with the political views of the user. For example, Osmundsen et al (2021a) show that hostility towards political opponents drives misinformation sharing, Rathje, Van Bavel, and van der Linden (2021) find that such out-group language strongly predicts social media engagement, and Brady et al (2017) and Valenzuela, Piña, and Ramírez (2017) find that moral-emotional language in political messages increases their diffusion.…”

Section: Related Workmentioning

confidence: 99%

Forecasting Political News Engagement on Social Media

Shivaram,

Bilgic,

Shapiro

et al. 2024

ICWSM

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Understanding how political news consumption changes over time can provide insights into issues such as hyperpartisanship, filter bubbles, and misinformation. To investigate long-term trends of news consumption, we curate a collection of over 60M tweets from politically engaged users over seven years, annotating ~10% with mentions of news outlets and their political leaning. We then train a neural network to forecast the political lean of news articles Twitter users will engage with, considering both past news engagements as well as tweet content. Using the learned representation of this model, we cluster users to discover salient patterns of long-term news engagement. Our findings include the following: (1) hyperpartisan users are more engaged with news; (2) right-leaning users engage with contra-partisan sources more than left-leaning users; (3) topics such as immigration, COVID-19, Islamaphobia, and gun control are salient indicators of engagement with low quality news sources.

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“…BOW is a text representation that describes the frequency with which words appear in a document. The (n=2/22) methods of feature extraction for tweet text methods were based on BOW [14,62]. In [14], the study explores machine learning approaches using word embeddings such as a distributed bag of words (DBOW), distributed memory means (DMM), and the performance of Word2vec convolutional neural networks (CNNs) to classify online hate, Word2Vec is defined as a distributed representation of words in a vector space that is used to aid learning algorithms in NLP tasks by grouping similar phrases.…”

Section: Bag Of Word (Bow)mentioning

confidence: 99%

“…The neural network achieved an accuracy of 95.33% for Dataset 1 and an accuracy of 96.38% for Dataset 2. In [62], hyper-partisan news was shared from two angles: (1) the features that make hyper-partisan content shareable and (2) the user motivations that drive the process. The study looks at one week's worth of Infowars.com content that was shared on Twitter and it was discovered that human interest and conflict in news stories drive the sharing process from a content standpoint, using both manual coding news material and semi-automated clustering of Twitter account descriptions.…”

Section: Bag Of Word (Bow)mentioning

confidence: 99%

Feature extractions and selection of bot detection on Twitter A systematic literature review

Al-azawi

AL-mamory²

2022

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Automated or semiautomated computer programs that imitate humans and/or human behavior in online social networks are known as social bots. Users can be attacked by social bots to achieve several hidden aims, such as spreading information or influencing targets. While researchers develop a variety of methods to detect social media bot accounts, attackers adapt their bots to avoid detection. This field necessitates ongoing growth, particularly in the areas of feature selection and extraction. The study's purpose is to provide an overview of bot attacks on Twitter, shedding light on issues in feature extraction and selection that have a significant impact on the accuracy of bot detection algorithms, and highlighting the weaknesses in training time and dimensionality reduction. To the best of our knowledge, this study is the first systematic literature review based on a preset search-strategy that encompasses literature published between 2018 and 2021 which are concerned with Twitter features (attributes). The key findings of this research are threefold. First, the paper provides an improved taxonomy of feature extraction and selection approaches. Second, it includes a comprehensive overview of approaches for detecting bots in the Twitter platform, particularly machine learning techniques. The percentage was calculated using the proposed taxonomy, with metadata, tweet text, and merging (meta and tweet text) accounting for 37%, 31%, and 32%, respectively. Third, some gaps are also highlighted for further research. The first is that public datasets are not precise or suitable in size. Second, the use of integrated systems and real-time detection is uncommon. Third, detecting each bots category identified separately is needed, rather than detecting all categories of bots using one generic model and the same features' values. Finally, extracting influential features that assist machine learning algorithms in detecting Twitter bots with high accuracy is critical, especially if the type of bot is pre-determined.

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Shareworthiness and Motivated Reasoning in Hyper-Partisan News Sharing Behavior on Twitter

Cited by 14 publications

References 44 publications

Medical Crowdfunding Campaign Sharing Behaviour on Mobile Social Media

Medical Crowdfunding Campaign Sharing Behaviour on Mobile Social Media

Forecasting Political News Engagement on Social Media

Feature extractions and selection of bot detection on Twitter A systematic literature review

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