Analyzing User Activities, Demographics, Social Network Structure and User-Generated Content on Instagram

Instagram is a relatively new form of communication where users can instantly share their current status by taking pictures and tweaking them using filters. It has seen a rapid growth in the number of users as well as uploads since it was launched in October 2010. Inspite of the fact that it is the most popular photo sharing application, it has attracted relatively less attention from the web and social media research community. In this paper, we present a large-scale quantitative analysis on millions of users and pictures we crawled over 1 month from Instagram. Our analysis reveals several insights on Instagram which were never studied before: 1) its social network properties are quite different from other popular social media like Twitter and Flickr, 2) people typically post once a week, and 3) people like to share their locations with friends. To the best of our knowledge, this is the first in-depth analysis of user activities, demographics, social network structure and user-generated content on Instagram.Comment: 5 page

The Digital Architectures of Social Media: Comparing Political Campaigning on Facebook, Twitter, Instagram, and Snapchat in the 2016 U.S. Election

The present study argues that political communication on social media is mediated by a platform's digital architecture, defined as the technical protocols that enable, constrain, and shape user behavior in a virtual space. A framework for understanding digital architectures is introduced, and four platforms (Facebook, Twitter, Instagram, and Snapchat) are compared along the typology. Using the 2016 US election as a case, interviews with three Republican digital strategists are combined with social media data to qualify the studyies theoretical claim that a platform's network structure, functionality, algorithmic filtering, and datafication model affect political campaign strategy on social media

Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok

TikTok is a video-sharing social networking service, whose popularity is increasing rapidly. It was the world's second-most downloaded app in 2019. Although the platform is known for having users posting videos of themselves dancing, lip-syncing, or showcasing other talents, user-videos expressing political views have seen a recent spurt. This study aims to perform a primary evaluation of political communication on TikTok. We collect a set of US partisan Republican and Democratic videos to investigate how users communicated with each other about political issues. With the help of computer vision, natural language processing, and statistical tools, we illustrate that political communication on TikTok is much more interactive in comparison to other social media platforms, with users combining multiple information channels to spread their messages. We show that political communication takes place in the form of communication trees since users generate branches of responses to existing content. In terms of user demographics, we find that users belonging to both the US parties are young and behave similarly on the platform. However, Republican users generated more political content and their videos received more responses; on the other hand, Democratic users engaged significantly more in cross-partisan discussions.Comment: Accepted as a full paper at the 12th International ACM Web Science Conference (WebSci 2020). Please cite the WebSci version; Second version includes corrected typo

#mytweet via Instagram: Exploring User Behaviour across Multiple Social Networks

We study how users of multiple online social networks (OSNs) employ and share information by studying a common user pool that use six OSNs - Flickr, Google+, Instagram, Tumblr, Twitter, and YouTube. We analyze the temporal and topical signature of users' sharing behaviour, showing how they exhibit distinct behaviorial patterns on different networks. We also examine cross-sharing (i.e., the act of user broadcasting their activity to multiple OSNs near-simultaneously), a previously-unstudied behaviour and demonstrate how certain OSNs play the roles of originating source and destination sinks.Comment: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2015. This is the pre-peer reviewed version and the final version is available at http://wing.comp.nus.edu.sg/publications/2015/lim-et-al-15.pd

DeepCity: A Feature Learning Framework for Mining Location Check-ins

Online social networks being extended to geographical space has resulted in large amount of user check-in data. Understanding check-ins can help to build appealing applications, such as location recommendation. In this paper, we propose DeepCity, a feature learning framework based on deep learning, to profile users and locations, with respect to user demographic and location category prediction. Both of the predictions are essential for social network companies to increase user engagement. The key contribution of DeepCity is the proposal of task-specific random walk which uses the location and user properties to guide the feature learning to be specific to each prediction task. Experiments conducted on 42M check-ins in three cities collected from Instagram have shown that DeepCity achieves a superior performance and outperforms other baseline models significantly

Language in Our Time: An Empirical Analysis of Hashtags

Hashtags in online social networks have gained tremendous popularity during the past five years. The resulting large quantity of data has provided a new lens into modern society. Previously, researchers mainly rely on data collected from Twitter to study either a certain type of hashtags or a certain property of hashtags. In this paper, we perform the first large-scale empirical analysis of hashtags shared on Instagram, the major platform for hashtag-sharing. We study hashtags from three different dimensions including the temporal-spatial dimension, the semantic dimension, and the social dimension. Extensive experiments performed on three large-scale datasets with more than 7 million hashtags in total provide a series of interesting observations. First, we show that the temporal patterns of hashtags can be categorized into four different clusters, and people tend to share fewer hashtags at certain places and more hashtags at others. Second, we observe that a non-negligible proportion of hashtags exhibit large semantic displacement. We demonstrate hashtags that are more uniformly shared among users, as quantified by the proposed hashtag entropy, are less prone to semantic displacement. In the end, we propose a bipartite graph embedding model to summarize users' hashtag profiles, and rely on these profiles to perform friendship prediction. Evaluation results show that our approach achieves an effective prediction with AUC (area under the ROC curve) above 0.8 which demonstrates the strong social signals possessed in hashtags.Comment: WWW 201

The effectiveness of content marketing activities in Facebook and Instagram : generating leads and improving sales for a B2C experiential service company

Nowadays a majority of society is using social media, which increased the number of social network sites (SNS) users. Business to consumer (B2C) companies have started to use SNS in order to gain from its close connectivity to customers and to attract new customers. Becoming of most self-interest to master the art of online marketing through SNS for B2C companies. The aim of this thesis is to study the effectiveness of visual user-generated content (UGC) versus own branded content for sales conversions on Facebook. Additionally, the study compares the effectiveness of own branded visual content vs UGC in generating leads on Instagram and Facebook. By analyzing the effectiveness of content marketing activities on SNS, we are contributing to the discussion on strategic marketing decisions for B2C enterprises. Having an interaction effect between the type of social network platform and type of content that is not statistically significant different, we can truthfully reject hypothesis one. The difference between conversion rates indicates that UGC inspires more confidence, achieving higher conversions rates over the five days the campaign occurred. Although the sample size was restricted which can affect the power of detecting meaningful difference and not allowing a statement on significance.Atualmente, a maior parte da nossa sociedade usa redes sociais, o que aumentou o número de usuários de redes sociais. Muitas empresas (B2C) começaram a usar redes sociais para contribuir ao bem do negocio. Tornando-se do maior interesse saber dominar a arte do marketing online através de redes sociais, estas plataformas sendo Facebook e Instagram. O objetivo desta tese é estudar a eficácia do conteúdo visual gerado por usuários (UGC) versus o conteúdo de marca própria para conversões de vendas no Facebook. Além disso, o estudo compara a eficácia do conteúdo visual de marca própria versus o de usuários (UGC) na geração de leads no Instagram e no Facebook. Ao analisar a eficácia das atividades de marketing com o conteúdo das redes sociais contribuindo para a discussão sobre decisões estratégicas de marketing para as empresas (B2C). Resultados revelam um efeito de interação entre o tipo de rede social e o tipo de conteúdo que não é estatisticamente significativo, rejeitando a hipótese 1. A diferença entre as quotas de conversão indica que o conteúdo visual gerado por usuários (UGC) inspira mais confiança, alcançando taxas de conversão mais altas nos cinco dias em que a campanha ocorreu. Embora o tamanho da amostra tenha sido restrito, o que pode afetar o poder de detetar diferenças significativas e não permitindo uma declaração com significância

Online Popularity and Topical Interests through the Lens of Instagram

Online socio-technical systems can be studied as proxy of the real world to investigate human behavior and social interactions at scale. Here we focus on Instagram, a media-sharing online platform whose popularity has been rising up to gathering hundred millions users. Instagram exhibits a mixture of features including social structure, social tagging and media sharing. The network of social interactions among users models various dynamics including follower/followee relations and users' communication by means of posts/comments. Users can upload and tag media such as photos and pictures, and they can "like" and comment each piece of information on the platform. In this work we investigate three major aspects on our Instagram dataset: (i) the structural characteristics of its network of heterogeneous interactions, to unveil the emergence of self organization and topically-induced community structure; (ii) the dynamics of content production and consumption, to understand how global trends and popular users emerge; (iii) the behavior of users labeling media with tags, to determine how they devote their attention and to explore the variety of their topical interests. Our analysis provides clues to understand human behavior dynamics on socio-technical systems, specifically users and content popularity, the mechanisms of users' interactions in online environments and how collective trends emerge from individuals' topical interests.Comment: 11 pages, 11 figures, Proceedings of ACM Hypertext 201