Identifying Attractive News Headlines for Social Media

Kourogi, Sawa; Fujishiro, Hiroyuki; Kimura, Atsushi; Nishikawa, Hitoshi

doi:10.1145/2806416.2806631

Cited by 6 publications

(9 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The correct choice of words in a heading is not trivial, because the optimal choice of words is constantly changing and depends on topics (Kim et al, ). In general, headlines should be precise, striking and expressive (Nwala & Umukoro, ) and arouse individual curiosity (Kourogi, Fujishiro, Kimura, & Nishikawa, ). Curiosity can be generated, for example, using pronouns.…”

Section: Influencing Factorsmentioning

confidence: 99%

“…Especially in connection with cultural differences, this point must be considered (Dor, ), because individuals in different cultures show a different sharing behavior (Chung & Darke, ). Kourogi et al () found that headings should contain as few characters as possible. In addition, keywords such as “breaking” and “new” attract attention.…”

Section: Influencing Factorsmentioning

confidence: 99%

“…They found that the interaction of the right title (length, description, sentence structure, and chosen word type), at the right time (between 10 a.m. and 4 p.m.) and in the right community (multiple uses of the same content in the same community has a negative impact on distribution) is essential for widespread dissemination. Images are another factor influencing the intention to share, as they can greatly increase the virality of a content (Kourogi et al, ). Content including an image is shared three times as often as content based on plain text.…”

Section: Influencing Factorsmentioning

confidence: 99%

“…Images are a commonly used content type in social networks, and their relevance increases with the growth of popular networks, such as Instagram. It is known that images increase the virality of news in social networks (Guerini et al, ; Kourogi et al, ). In their analysis, Deza and Parikh () categorized the images shared on the Reddit platform according to their virality.…”

Section: Influencing Factorsmentioning

confidence: 99%

See 3 more Smart Citations

The decision‐making process in viral marketing—A review and suggestions for further research

Reichstein

Brusch

2019

Psychology and Marketing

View full text Add to dashboard Cite

Viral marketing is used to widely distribute content. To achieve this goal, the basic decision‐making process from content reception to interaction must be clarified. This paper examines the decision‐making process of individuals in viral marketing using a new dynamic model. In addition, this work reviews the existing literature on viral marketing and structures to identify existing issues for further research. The decision‐making process is basically divided into two stages. In the first decision stage, individuals decide whether content should be considered. When individuals agree to view the content, they decide in the second stage whether they want to interact with it. These two decisions are influenced by three factors: the framework conditions, content, and interaction aims. With the help of the decision model, this paper summarizes the most important findings from viral marketing research over the last 20 years. In addition, this work provides new opportunities for further research in the field of viral marketing.

show abstract

Section: Influencing Factorsmentioning

confidence: 99%

Section: Influencing Factorsmentioning

confidence: 99%

Section: Influencing Factorsmentioning

confidence: 99%

Section: Influencing Factorsmentioning

confidence: 99%

See 2 more Smart Citations

The decision‐making process in viral marketing—A review and suggestions for further research

Reichstein

Brusch

2019

Psychology and Marketing

View full text Add to dashboard Cite

show abstract

“…Other studies have found a more direct connection between user content and news. For example, Tatar et al (2014) used the comments posted by users to rank news articles and infer their popularity, and Kourogi et al (2015) proposed a model that suggests attractive news headlines to share on social media.…”

Section: Related Workmentioning

confidence: 99%

Unified Representation of Twitter and Online News Using Graph and Entities

et al. 2021

View full text Add to dashboard Cite

To improve consumer engagement and satisfaction, online news services employ strategies for personalizing and recommending articles to their users based on their interests. In addition to news agencies’ own digital platforms, they also leverage social media to reach out to a broad user base. These engagement efforts are often disconnected with each other, but present a compelling opportunity to incorporate engagement data from social media to inform their digital news platform and vice-versa, leading to a more personalized experience for users. While this idea seems intuitive, there are several challenges due to the disparate nature of the two sources. In this paper, we propose a model to build a generalized graph of news articles and tweets that can be used for different downstream tasks such as identifying sentiment, trending topics, and misinformation, as well as sharing relevant articles on social media in a timely fashion. We evaluate our framework on a downstream task of identifying related pairs of news articles and tweets with promising results. The content unification problem addressed by our model is not unique to the domain of news, and thus can be applicable to other problems linking different content platforms.

show abstract