Tweeting the Mind and Instagramming the Heart: Exploring Differentiated Content Sharing on Social Media

Manikonda, Lydia; Meduri, Venkata Vamsikrishna; Kambhampati, Subbarao

doi:10.1609/icwsm.v10i1.14819

Cited by 21 publications

(13 citation statements)

References 12 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, this research created a system that can determine the topic of a caption on Instagram. Manikonda et al [2016] was one of the critical studies exploring Instagram and Twitter users, concerning user activities and demographics. Identifying a topic of discussion of social media is not an easy task, as the text were available in large volume Arora et al [2018], unstructured and informal Kang and Lee [2017].…”

Section: Author 1 Introductionmentioning

confidence: 99%

See 1 more Smart Citation

What is inside the mind of teenagers on Instagram

Rakhmawati

Valianta

Hafidz

et al. 2021

IJBIS

View full text Add to dashboard Cite

Instagram is one of the most used social media with 45 million active users on each month. In Indonesia, the highest number of internet users is between the age of 13-34 years old. It shows that most of the new internet users are young people. In this research, we create a topic model for the Instagram caption of teenager users in Surabaya, Indonesia, using LDA (Latent Dirichlet Allocation) method. By using pen and paper questionnaire, we collected total number of Instagram 494 valid accounts with 4664 captions. The data were collected from January 2014 to June 2017. The process of modelling using LDA was performed by experimenting with a set of number of topics: 2,3,4 and 5. The 2 topics were selected because it has a small value of perplexity, which indicates that the model has a good level of conformity. The two topics represents two categories: "school" and "relationship". It was found that the topic model was dominated by the "relationship" category.

show abstract

Section: Author 1 Introductionmentioning

confidence: 99%

“…Some previous studies have successfully used LDA to create a topic model. Manikonda et al [2016] has revealed topic modelling in Instagram and Twitter. Liu and Jansson [2017] exploits LDA to create a topic model of an Instagram caption in English that reveals some topics as well as sentiments analysis.…”

Section: Author 1 Introductionmentioning

confidence: 99%

What is inside the mind of teenagers on Instagram

Rakhmawati

Valianta

Hafidz

et al. 2021

IJBIS

View full text Add to dashboard Cite

show abstract

“…Related work has focused on differences in sentiment analysis of content generated across platforms. For instance, while examining the posts posted by the same group of users on Instagram and Twitter, (Manikonda, Meduri, and Kambhampati 2016) saw that posts on Twitter contain more negative expressions than posts on Instagram. (Ali et al 2023) also argued that meta-data features (e.g., conversation length) were better predictors of risky conversations on Instagram.…”

Section: Related Workmentioning

confidence: 99%

“…Although many works have been studying linguistic differences on multiple platforms, no work has explored the linguistic differences for harmful content posted by communities across multiple platforms, which is a gap our work fills. Moreover, existing tools for cross-platform comparison are limited to sentiment analysis and conventional topic modeling next to temporal frequency counts (e.g., the number of comments with negative sentiment (Manikonda, Meduri, and Kambhampati 2016;Lin and Qiu 2013;Ruan et al 2022), or the number of links to deleted YouTube videos (Yang et al 2021)). Our study goes beyond sentiment analysis and makes nuanced comparisons across several axes.…”

Section: Related Workmentioning

confidence: 99%

Differences in the Toxic Language of Cross-Platform Communities

Singh,

Ghafouri,

Such

et al. 2024

ICWSM

View full text Add to dashboard Cite

Cross-platform communities are social media communities that have a presence on multiple online platforms. One active community on both Reddit and Discord is dankmemes. Our study aims to examine differences in harmful language usage across different platforms in a community. We scrape 15 communities that are active on both Reddit and Discord. We then identify and compare differences in type and level of toxicity, in the topics of the harmful discourse, in the temporal evolution of toxicity and its attribution to users, and in the moderation strategies communities across platforms. Our results show that most communities exhibit differences in toxicity depending on the platform. We see that toxicity is rooted in the different subcultures as well as in the way in which the platforms operate and their administrators moderate content. However, we note that in general terms Discord is significantly more toxic than Reddit. We offer a detailed analysis of the topics and types of communities in which this happens and why, which will help moderators and policymakers shape their strategies to mitigate the harm on the Web. In particular, we propose practical and effective strategies that Discord can implement to improve its platform moderation.

show abstract

“…On the other hand, platforms such as Instagram also generate a lot of content centred around social activities (de Juan et al, 2021). Activity on Twitter's platform is primary focused around topical discourse (Manikonda et al, 2021). These framings have an effect on the type of preferences expressed through different sources of crowdsourced data because users work towards the social norms established within the online space (Calcagni et al, 2019;Venturelli et al, 2017).…”

Section: Purposementioning

confidence: 99%

Crowdsourced data and machine learning to measure cultural ecosystem services

Havinga

View full text Add to dashboard Cite

Cultural ecosystem services (CES) are some of the most valuable contributions of ecosystems to human well-being. Nevertheless, these services are often underrepresented in ecosystem service assessments. Defining CES for the purposes of spatial quantification has been challenging because it has been difficult to spatially model CES. However, rapid increases in mobile network connectivity and the use of social media have generated huge amounts of crowdsourced data. This offers an opportunity to define and spatially quantify CES. We inventoried established CES conceptualisations and sources of crowdsourced data to propose a CES definition and typology for spatial quantification. Furthermore, we present the results of three spatial models employing crowdsourced data to measure CES on Texel, a coastal island in the Netherlands. Defining CES as information-flows best enables service quantification. A general typology of eight services is proposed. The spatial models produced distributions consistent with known areas of cultural importance on Texel. However, user representativeness and measurement uncertainties affect our results.Ethical considerations must also be taken into account. Still, crowdsourced data is a valuable source of information to define and model CES due to the level of detail available. This can encourage the representation of CES in ecosystem service assessments.

show abstract

Tweeting the Mind and Instagramming the Heart: Exploring Differentiated Content Sharing on Social Media

Cited by 21 publications

References 12 publications

What is inside the mind of teenagers on Instagram

What is inside the mind of teenagers on Instagram

Differences in the Toxic Language of Cross-Platform Communities

Crowdsourced data and machine learning to measure cultural ecosystem services

Contact Info

Product

Resources

About