Popularity Of Posts Research Articles

Multi‐Pop: Enhancing user engagement with content‐based multimodal popularity prediction in social media

AbstractSocial media has entrenched itself as an indispensable marketing tool. We introduce a quantitative approach to predicting the popularity of social media posts within the café and bakery sector. Employing Multi‐Pop, a multimodal popularity prediction model that harnesses both images and text from post content, it utilizes the features of posts that significantly influence their popularity on one of the most widely used platforms, Instagram. By focusing solely on post‐content features and excluding user information, we analysed 8765 Instagram posts from the cafe and bakery domain, revealing that our model attains a superior accuracy rate of 82.0% compared with existing popularity prediction methods. Furthermore, the study identifies hashtags and post captions as exerting a greater impact on post popularity than images. This research furnishes valuable insights, particularly for small business owners and individual entrepreneurs, by introducing novel computational and empirical methodologies for Instagram marketing strategy and post popularity prediction, thereby enhancing the comprehension of social media marketing dynamics.

How foodstagramming posts influence restaurant visit intention: the mediating role of goal relevance and mimicking desire

PurposeThis study aims to explore the role of social media visual posts (known as foodstagramming) on restaurant visit intention. Drawing on the heuristic–systematic model and normative focus theory, this research introduces a framework that assesses the effects of key foodstagramming attributes – vicarious expression, aesthetic appeal and post popularity – and the mediating roles of goal relevance and mimicking desire, in the process.Design/methodology/approachStructural equation modelling was performed to test the proposed model using a sample of tourists (n = 377) and residents (n = 341). Multi-group analysis was performed to compare the differences between these groups.FindingsResults reveal that mimicking desire and goal relevance influence restaurant visit intention; however, mimicking desire has a stronger influence than goal relevance. Little difference was found between the tourist and the resident groups in the proposed relationships, except that vicarious expression positively influences mimicking desire in the tourist group but not in the resident group.Practical implicationsThis study guides restauranteurs and social media influencers (foodstagrammers). It shows that consumers value the textual content and aesthetic appeal of photos over the popularity of a post. It also indicates that vicarious expression is more important for tourists than for residents.Originality/valueThis research advances social media marketing literature by proposing a new information processing framework. To the best of the authors’ knowledge, this study is one of the first studies to explore the impact of visual post attributes on individual decision-making behaviours through socially acceptable norms.

Decoding digital engagement: a comparative analysis of English and Turkish brand post popularity dynamics on platform X

PurposeThis research aims to understand the dynamics that drive consumer engagement of multinational brands' social media posts on platform X, formerly known as Twitter. Taking the emotional tone of posts into account, the effect of vivid, interactive, informative, entertaining and practical features of posts on consumer interactions are evaluated across English- and Turkish-speaking markets.MethodologyInspired by the conceptual framework proposed in previous literature, features were extracted computationally using natural language processing from platform X posts of 33 Fortune 500 brands from various industries from June 2016 to June 2021. Following evaluation of regression models on alternative distributions of the dependent variable, which is total number of likes, shares and comments, random subspace regression using bootstrap resampling was applied to calculate an importance score and evaluate the effect of features.FindingsConsumers in English- and Turkish-speaking markets perceive and engage with content differently. While informative and entertaining posts resonate more with English speakers, emotions play a broader role for Turkish speakers. English-speaking audience prefers happy and vivid daytime messages with questions, while Turkish-speaking audience is drawn to angry messages, lean toward nighttime posts.OriginalityThis research is a pioneer to evaluate the factors that influence brands' platform X post engagements across markets of different cultural orientation. Beyond assessing the distinctions in brand post elements, the role of emotional content in brand messages were also analyzed across English- and Turkish-speaking markets.

Like, Share, and Follow! Analysis of the Use of Social Media by Pathology Residency Programs

Abstract Introduction/Objective To establish the patterns of social media usage across Facebook, Twitter, and Instagram for pathology residency programs and departments from January 2018 to August 2022. To our knowledge, this is the first study of its kind focused on pathology residency programs. Methods/Case Report A list of pathology residency programs was compiled from National Resident Matching Program data. Websites for pathology residencies and major social media platforms were searched for pathology program accounts. The date of creation, number of followers, and number of likes were tracked for each program’s account(s). Each post between January 2018 and August 2022 was categorized according to the content (i.e resident life, education, research announcements, etc.), the number of likes, comments, and shares. Data collection is ongoing with future plans to compare posts and social media popularity between programs based on residency program size, prestige, and geographic location. Results (if a Case Study enter NA) Preliminarily 86 residency programs and 110 social media accounts were identified. There was an increase in social media posts between 2018 and 2022 with Twitter being the most utilized platform. The most commonly posted content category was pathology education, particularly on Twitter. Programs can use this information to create education-based posts or expand on existing teaching programs through social media. While educational posts had the most likes on Facebook, posts focused on the MATCH (the process in which medical students are accepted into a residency program) garnered the most likes on Twitter and Instagram. Conclusion A closer look into how residency programs are using social media and the relative success of posts could be used to increase visibility of residency programs as the resident recruitment process continues to be conducted virtually. The prevalence and popularity of education-focused posts shows that social media is an effective platform to teach pathology and is well received by viewers. Future research can focus on how pathology residency social media posts may have impacted residents, students, and other social media consumers on their decision to apply or to attend a program and/or interest in pathology.

Factors Influencing the Answerability and Popularity of a Health-Related Post in the Question-and-Answer Community: Infodemiology Study of Metafilter.

The web-based health question-and-answer (Q&A) community has become the primary and handy way for people to access health information and knowledge directly. The objective of our study is to investigate how content-related, context-related, and user-related variables influence the answerability and popularity of health-related posts based on a user-dynamic, social network, and topic-dynamic semantic network, respectively. Full-scale data on health consultations were acquired from the Metafilter Q&A community. These variables were designed in terms of context, content, and contributors. Negative binomial regression models were used to examine the influence of these variables on the favorite and comment counts of a health-related post. A total of 18,099 post records were collected from a well-known Q&A community. The findings of this study include the following. Content-related variables have a strong impact on both the answerability and popularity of posts. Notably, sentiment values were positively related to favorite counts and negatively associated with comment counts. User-related variables significantly affected the answerability and popularity of posts. Specifically, participation intensity was positively related to comment count and negatively associated with favorite count. Sociability breadth only had a significant impact on comment count. Context-related variables have a more substantial influence on the popularity of posts than on their answerability. The topic diversity variable exhibits an inverse correlation with the comment count while manifesting a positive correlation with the favorite count. Nevertheless, topic intensity has a significant effect only on favorite count. The research results not only reveal the factors influencing the answerability and popularity of health-related posts, which can help them obtain high-quality answers more efficiently, but also provide a theoretical basis for platform operators to enhance user engagement within health Q&A communities.

Examining visual impact: predicting popularity and assessing social media visual strategies for NGOs

Abstract Purpose This research aims to analyze the role of visuals posted on the social media of NGOs and to predict the popularity of a post based on the characteristics of the visual it contains. Design/methodology/approach Two social media platforms, namely Facebook and Instagram, were selected as the empirical study environments. Specifically, all visuals posted on 12 child-related Non-Government Organizations during the period of 2020–2021 (4,144 in total) were collected and subsequently subjected to automatic characterization using visual recognition and artificial intelligence tools. Machine learning algorithms were then employed to predict the popularity of a post solely based on the visuals it contains, as well as to identify the most significant features that serve as predictors for post popularity. Findings The Support Vector Classifier performed best with a prediction accuracy of 0.62 on Facebook and 0.81 on Instagram. For the explanation of the model, we used feature importance metrics and found that features like the presence of people and the emotions of joy and calmness are important for the prediction. Practical implications Companies and organizations serve a large part of their communication strategy through social media. Given that every advertiser would like to use their funds in the most efficient way, the ability to predict the performance of a post would be a very important tool. Social implications The methodology can be used in the non-profit sector, whereby knowing what visual will perform better they could promote their mission more effectively, increase public awareness, raise funds and reduce expenses on their communication strategy. Originality/value The novelty of this work regarding popularity prediction on social media lies in the fact that to make the prediction, it focused exclusively on the visual and its characteristics and achieved high accuracy scores in the case of Instagram. Additionally, it provided important information about visual characteristics and their importance in predicting popularity.

Application of sensor technology in grasping and preprocessing of network hotspot information propagation

The rapid dissemination of hot information on the Internet has become a common phenomenon in today's society. Traditional methods of information capture and preprocessing often require a lot of manpower and material resources, and the captured information has low timeliness and accuracy. The purpose of this paper was to use sensor technology to find and locate network hotspots in time. By collecting user generated content, social media data, news reports, etc., the data is analyzed and mined to identify popular topics and events. In terms of information capture, sensor technology can monitor and understand user activities, the popularity of posts, emotional tendencies, user attention, user interaction, etc., through information monitoring. Network data was collected, such as network latency, data loss rate, and bandwidth utilization. Sensor technology was used to collect social media data to understand the level of public attention to hot events. In information preprocessing, sensor technology was used to remove noise and redundant information in data to ensure data quality. The data were labeled and classified, and the information dissemination rules of network hotspot were analyzed in depth. The average capture accuracy of Method 1 for Hotspot 1, Hotspot 2, and Hotspot 3 was 72.11%, 71.81%, and 72.54%, respectively. The average capture accuracy of Method 2 for Hotspot 1, Hotspot 2, and Hotspot 3 was 82.55%, 83.14%, and 82.91%, respectively. When the data was 40, 80, and 120, the preprocessing times of Method 1 for Post 1 were 8.81 s, 15.47 s, and 18.77 s, respectively. The preprocessing times of Method 2 for Post 1 were 5.97 s, 7.80 s, and 9.25 s, respectively. The application of sensor technology in the capture and preprocessing of network hot information dissemination has brought a variety of innovations, including multi-modal data acquisition, real-time monitoring and analysis, user behavior analysis, data cleaning and integration, anomaly detection and early warning, intelligent recommendation and personalized service, etc., thus improving the accuracy, real-time and personalized degree of information acquisition.

What's trending? Reach and content of the Society for Maternal-Fetal Medicine on social media

The Society for Maternal-Fetal Medicine (SMFM) uses social media to increase awareness of the Society and its key programs and to foster community and discussion around perinatal health, especially on Twitter. The influence and role of the SMFM Twitter account in public discourse around issues relevant to pregnancy have not been studied. We sought to evaluate trends in engagement with SMFM on Twitter by analyzing SMFM follower growth and discussion topics on Twitter compared with Facebook and by quantifying public engagement during the SMFM Annual Pregnancy Meeting. This retrospective study analyzed follower growth data from August 2019 to July 2022 for SMFM Twitter (@MySMFM) and SMFM Facebook (@SocietyforMaternalFetalMedicine). We identified the top 10 Tweets and Facebook posts during the study period using Twitter analytics and Facebook data. Popularity of Tweets and Facebook posts was determined by "impressions" and "reach", respectively; these metrics reflect the number of times a post was viewed. To evaluate annual trends in SMFM Twitter engagement, we analyzed data associated with the SMFM Annual Pregnancy Meeting, including the number of Tweets using the hashtag (#SMFM(Year)) and overall impressions for the SMFM Twitter account for each meeting from 2016-20223. The absolute number of new followers for SMFM Twitter and Facebook was similar, but the relative increase and rate of follower growth was higher for Twitter than for Facebook. The Twitter account consistently gained followers while the Facebook account experienced intermittent periods of stagnancy or follower loss. More than half of the top ranked posts on Twitter and Facebook mentioned the COVID vaccine; other popular topics included COVID-19 and abortion. During the SMFM Annual Pregnancy Meeting, Tweets using the meeting hashtag consistently peaked on meeting day 4, coincident with the opening plenary session (mean number of Tweets = 1270 +/- 499). An upward trend in Annual Pregnancy meeting tweets was observed each year until 2021 - the first virtual SMFM meeting. Trends in SMFM Twitter engagement suggest increasing use and popularity of the platform for timely dissemination of pregnancy-related news, guidelines, and research. The reduction in Annual Pregnancy Meeting Tweets and impressions in 2021 and 2022 suggests the potential negative impact of virtual meetings on SMFM member engagement around Annual Meeting content.

Elephant 'selfies': Evaluating the effectiveness of Instagram's warning of the potential negative impacts of photo opportunities with wild animals.

Wildlife tourist attractions offering opportunities to observe, touch, and interact with wild animals, are visited by millions of people every year. Wildlife tourism has considerable economic value in many countries and can have positive impacts on wild animal populations (e.g. through habitat protection); it can also have negative impacts on population conservation and individual welfare (due to, e.g. habitat encroachment, disturbance, or disease). The recent phenomenon of 'wildlife selfies' shared on social media may seem harmless but can involve animals illegally or unsustainably captured from the wild, kept in poor conditions, or subject to cruel treatment. To address this issue, Instagram introduced a pop-up alert system that is triggered when users search for wild animal selfie hashtags (e.g. #elephantselfie), warning of the potential negative impacts of wildlife selfies on wild animals. Using elephant selfies as a case study, we found that Instagram's alert was triggered by only 2% of 244 elephant selfie-related hashtags tested. By comparing three pairs of similar hashtags (one of each pair that triggered the warning and one that did not), we were unable to detect a consistent difference in the type of post using each of the hashtags, the popularity of posts, or the sentiment of viewer comments. The warning is not shown when posting an image, or if a post is viewed directly by a follower, only if the post is encountered via a hashtag search. Currently, what is portrayed on social media appears to be inconsistent with apparent recent shifts in social acceptibilty regarding tourism, particularly as concerns direct contact between tourists and elephants. Instagram's wildlife selfie initiative was commendable but given its apparent lack of effect, we urge Instagram and other social platforms to do more to prevent harmful content from being posted on their platforms and to promote fair, ethical and sustainable interactions between wild animals and people.

Characterizing Heated Tobacco Products Marketing on Instagram: Observational Study.

Heated tobacco products (HTPs), including I Quit Ordinary Smoking (IQOS), are new tobacco products that use an electronic device to heat compressed tobacco leaves to generate an aerosol for consumers to inhale. Marketing of HTPs is prevalent on Instagram, a popular social media platform. This study aims to characterize posts related to HTPs on Instagram and their associations with user engagement. Through the Instagram application programming interface, 979 Instagram posts were collected using keywords related to HTPs, such as "IQOS" and "heat-not-burn." Among them, 596 posts were related to IQOS and other HTP marketing. The codebook was developed from a randomly selected 200 posts on the post content by hand coding, which was applied to the remaining 396 Instagram posts. Summary statistics were calculated, and statistical hypothesis testing was conducted to understand the popularity of Instagram posts on HTPs. Negative binomial regression models were applied to identify Instagram post characteristics associated with user engagement (eg, count). Among Instagram posts related to HTP marketing (N=596), "product display" was dominant (n=550, 92.28%), followed by "brand promotion" (n=41, 6.88%), and "others" (n=5, 0.84%). Among posts within "product display," "device only" was the most popular (n=338, 61.45%), followed by "heatstick only" (n=80, 14.55%), "accessory" (n=66, 12%), "device and heatstick" (n=56, 10.18%), and "capsule" (n=10, 1.82%). A univariate negative binomial regression model with pairwise comparisons across "product display" types showed that the number of likes for posts with HTP heatsticks was significantly lower compared to posts with HTP devices, accessories, and device-heatstick sets. Multivariate negative binomial regression models showed that HTP-related Instagram posts with a model or lifestyle elements (;=.60, 95% CI 0.36-0.84) or without obvious product advertising information (=.69, 95% CI 0.49-0.89) received more likes. It is shown that posts with product displays were dominant among HTP-related posts on Instagram. Posts with model or lifestyle elements are associated with high user engagement, which might be one of the web-based marketing strategies of HTPs.

Social media popularity prediction with multimodal hierarchical fusion model

With the development of social media, data are becoming increasingly diversified, including images, texts and videos, which also makes it possible to use multimodal information to predict popularity. To effectively predict the popularity of specific posts in social networks, we propose a social media popularity prediction framework based on a hierarchical fusion model. We first perform feature extraction on four kinds of information for two modalities (image, image attribute, text and text attribute). Here, we extract image features with Residual Nets (ResNet) and generate image attribute features with an image attribute predictor. We use the image attribute features, Global Vectors for Word Representation (GloVe) embeddings and the bi-directional Long Short-Term Memory (LSTM) model to obtain text features, and the text attribute features are added through data preprocessing. Then, the features of the kinds of four information are reconstructed with our multimodal hierarchical fusion model and merged into a feature vector for popularity prediction. Experimental results show that our hierarchical fusion method has excellent performance for popularity prediction on two real-world datasets.

Hashtag recommendation for enhancing the popularity of social media posts.

Social media has gained huge importance in our lives wherein there is an enormous demand of getting high social popularity. With the emergence of many social media platforms and an overload of information, attaining high popularity requires efficient usage of hashtags, which can increase the reachability of a post. However, with little awareness about using appropriate hashtags, it becomes the need of the hour to build an efficient system to recommend relevant hashtags which in turn can enhance the social popularity of a post. In this paper, we thus propose a novel method hashTag RecommendAtion for eNhancing Social popularITy to recommend context-relevant hashtags that enhance popularity. Our proposed method utilizes the trending nature of hashtags by using post keywords along with the popularity of users and posts. With the prevalent evaluation techniques of this field being quite unreliable and non-uniform, we have devised a novel evaluation algorithm that is more robust and reliable. The experimental results show that our proposed method significantly outperforms the current state-of-the-art methods.

Data-driven influencer marketing strategy analysis and prediction based on social media and Google Analytics data

Due to various uncertainties on social media, data-driven strategy has become a necessity for influencer marketing. Typically, a promotional post by an influencer aims to direct the viewers to buy a product from a brand's website. The objective of this paper is to analyse the factors that contribute to the popularity of promotional posts in terms of likes and website visits count. This research utilised Facebook (FB), Instagram (IG) and Google Analytics (GA) data collected from the ambassadors (or influencers) of MeCan App, a Malaysian e-commerce company. The factors that contribute to popularity have been successfully identified, such as the optimal posting time, hashtags, image type, interval and ratio of posts. For example, they should post based on the ratio of one regular post for 5.4 promotional posts for the best exposure. Additionally, regression methods were implemented to predict website visit count, with an accuracy of 69.9 per cent using Random Forest Regressor.

Exploring visual relationship for social media popularity prediction

Social media popularity prediction is an important channel to explore content sharing and communication on social networks. It aims to capture informative cues by analyzing multi-type data (such as images, user profiles, and text) to decide the popularity of a specified post. Intuitively, given an image, humans can volitionally focus on salient objects and relationships that are associated with their interests. For example, when we see the image including the relationship “elephant-attack-van”, it is more natural to increase our interest than the image with “elephant-near-van”. Therefore, exploiting such structural relationships is expected to help the prediction model search for evidence in support of the popularity of posts. However, most current works only focus on the global representation or the isolated objects, while ignoring the structure knowledge contained in images. To address this problem, we propose the relationship-aware social media popularity predictor. First, we extract inter-object relationships via a pre-trained scene graph generator. Then, we design a content-based filtering module to filter redundant relationships and capture the key 〈subject–predicate–object〉 information. Finally, we integrate relationship information with multi-type heterogeneous data and feed them into the CatBoost model for regression. Moreover, our predictor is capable of generating more intuitive interpretations by analyzing visual relationships in images to reasonably infer popularity scores. Extensive experiments conducted on the Social Media Prediction Dataset demonstrate that the proposed method can outperform other state-of-the-art models. Additional ablation studies and visualizations further validate the effectiveness and interpretability.

The Role of Content Strategy in Social Media on Brand Post’s Popularity: A Case of Higher Education Institutions in Turkey

Abstract The highly competitive higher education market has turned to branding as a solution for dealing with today’s global challenges. One important tool of a sustainable brand strategy involves forming brand fan pages and attracting followers on social networking web sites. Implementing effective content strategy on brands’ fan pages can help to form and foster relationships with the target group. Thus the aim of this study is to understand the role that content strategy in social networking web sites plays in determining the popularity of brand posts’ created by leading higher education institutions in Turkey. Results indicate that the top ten higher education institutions in Turkey most commonly made posts representing the Text + image + link content type. The majority of posts to community members were created by the institutions themselves, involved university news and were primarily made on weekdays between noon and 18:00. Additionally, MANOVA analyses were conducted to investigate the role of various elements of content strategy on brand posts’ popularity. Results indicate that content type, content agility, content context, posting type, and posting day significantly impact the number of likes, shares and comments, which are indicators of brand posts’ popularity.

Mining and modelling temporal dynamics of followers’ engagement on online social networks

A relevant fraction of human interactions occurs on online social networks. In this context, the freshness of content plays an important role, with content popularity rapidly vanishing over time. We therefore investigate how influencers’ generated content (i.e., posts) attracts interactions, measured by the number of likes or reactions. We analyse the activity of influencers and followers over more than 5 years, focusing on two popular social networks: Facebook and Instagram, including more than 13 billion interactions and about 4 million posts. We investigate the influencers’ and followers’ behaviour over time, characterising the arrival process of interactions during the lifetime of posts, which are typically short-lived. After finding the factors playing a crucial role in the post popularity dynamics, we propose an analytical model for the user interactions. We tune the parameters of the model based on the past behaviour observed for each given influencer, discovering that fitted parameters are pretty similar across different influencers and social networks. We validate our model using experimental data and effectively apply the model to perform early prediction of post popularity, showing considerable improvements over a simpler baseline.

Brand post popularity on Facebook, Twitter, Instagram and LinkedIn: the case of start-ups

PurposeWith the growing trend of omni-channel marketing, brands are increasingly looking to offer a seamless experience to their online fan base by connecting with them across multiple social media platforms. This paper explores the relationship between brand posts' characteristics and popularity for start-ups across four different social media platforms: Facebook, Twitter, Instagram and LinkedIn.Design/methodology/approachA total of 1,200 social media posts from 10 start-ups were subjected to content analysis. Regression analysis was employed with brand posts' popularity (likes, comments and shares/retweets) as the dependent variable.FindingsThe results reveal several nuances in brand post popularity for start-ups across Facebook, Twitter, Instagram and LinkedIn. Antecedents of the popularity measures of likes, comments and shares/retweets also fared differently.Originality/valueThe paper reports one of the earliest empirical studies to better understand how the qualities of brand posts are related to their appeal across multiple social media platforms. It advances the literature on social media marketing and offers insights to social media managers of brands, particularly start-ups, on how to offer smoother customer journeys across numerous digital touchpoints.

Reconstruction of Unfolding Sub-Events From Social Media Posts

Event detection plays a crucial role in social media analysis, which usually concludes sub-event detection and correlation. In this article, we present a method for reconstructing the unfolding sub-event relations in terms of external expert knowledge. First, a Single Pass Clustering method is utilized to summarize massive social media posts. Second, a Label Propagation Algorithm is introduced to detect the sub-event according to the expert labeling. Third, a Word Mover’s Distance method is used to measure the correlation between the relevant sub-events. Finally, the Markov Chain Monte Carlo simulation method is presented to regenerate the popularity of social media posts. The experimental results show that the popularity dynamic of the empirical social media sub-events is consistent with the data generated by the proposed method. The evaluation of the unfolding model is 50.52% ∼ 88% higher than that of the random null model in the case of “Shanghai Tesla self-ignition incident.” This work is helpful for understanding the popularity mechanism of the unfolding events for online social media.

Judging a Book by Its Cover: Predicting the Marginal Impact of Title on Reddit Post Popularity

Several factors influence the popularity of content on social media, including the what, when, and who of a post. Of these factors, the what and when of content are easiest to customize in order to maximize viewership and reach. Further, the title of a post (part of the what) is the easiest to tailor, compared to the post's body, which is often fixed. So, in this paper, we assess the impact of a post's title on its popularity while controlling for the time of posting (the when) by proposing an interpretable attention-based model. Our approach achieves state-of-the-art performance on predicting the popularity of posts on multiple online communities of various sizes, topics, and formats while still being parsimonious. Interpretation of our model's attention weights sheds light on the heterogeneous patterns in how the specific words in a post's title shape its popularity across different communities. Our results highlight the power of sentiment alignment, personal storytelling, and even personality politics in propelling content to virality.

Metaphorical or Straightforward? Comparing the Effectiveness of Different Types of Social Media Advertising.

The existing studies have analyzed the advertising effects of metaphorical advertisements and straightforward adverts in traditional advertising media. However, their advertising effects on social media are still unclear. To address this issue, this study uses eye-tracking and questionnaires to examine two types of social media tourism advertising—metaphorical and straightforward in posts with both high and low popularity. This within-subject (n = 55) experiment was designed to examine the effects of social media tourism advertising types on visual attention and tourism intention and to identify the moderating role of post popularity based on the elaboration likelihood model (ELM). We found that advertising types had no significant effect on visual attention, but metaphorical advertisements increased tourism intention compared with straightforward adverts. Furthermore, we found that the level of post popularity moderated the effect of advertising types on visual attention. Specifically, metaphorical adverts in highly popular posts attracted more visual attention in the advertising text area and in the whole advert. Straightforward adverts in posts with low popularity attracted more visual attention in the advertising text area. This research advances the current literature by exploring the effects of social media tourism advertising types and has implications for managers deciding on strategies for social media tourism marketing.