Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们 横山亮次奖 百年刊庆
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  横山亮次奖  |  百年刊庆
清华大学学报(自然科学版)  2022, Vol. 62 Issue (1): 77-87    DOI: 10.16511/j.cnki.qhdxxb.2021.21.036
  专题:社会媒体处理 本期目录 | 过刊浏览 | 高级检索 |
用户参与视角下多图推文的图像位置和布局效应
马晓悦, 孟啸
西安交通大学 新闻与新媒体学院, 西安 710049
Image position and layout effects of multi-image tweets from the perspective of user engagement
MA Xiaoyue, MENG Xiao
School of Journalism and New Media, Xi'an Jiaotong University, Xi'an 710049, China
全文: PDF(1369 KB)   HTML
输出: BibTeX | EndNote (RIS)      
摘要 该文受用户认知研究启发,探究新浪微博平台中多图推文的图像位置与布局对图像内容向用户互动参与行为转化过程的影响。使用基于单图推文数据训练的XGBoost模型预测多图推文中各张图像所具有的“用户参与潜力”,通过相关分析、Z检验、OLS回归分析验证图像位置、布局与用户参与的关系。研究结果表明,在新浪微博平台的多图推文中,图像位置与布局因素能够在一定程度上影响用户互动参与,具体表现为:1)在图像位置效应中,包含2张、4张、5张和8张图像的推文具有右侧位置效应,包含6张、8张图像的推文具有底部位置效应,包含3张和8张图像的推文分别具有边缘效应和中间效应,其他大多情况下则为对称效应。2)在图像布局效应中,布局2、3、4、5、6、8相比单张图像能够正向促进图像的用户参与潜力转化,使得图像推文可能实现超出其内容潜力平均水平的用户参与度;但是布局7具有负面影响,布局9与单图推文无显著差异。研究结果可为社交媒体图像信息发布提供参考。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
马晓悦
孟啸
关键词 社交媒体视觉图像分布用户参与机器学习回归分析    
Abstract:Inspired by user cognition research, this study explores the image position and layout effects on the transformation process from image content to user engagement behavior in multi-image tweets on the Sina Weibo platform. The XGBoost model trained on the single-image tweet data was used to predict each image's "user engagement potential" in multi-image tweets. Correlation analysis, Z-test, and ordinary least squares (OLS) regression analysis were used to verify the relationship between image position, layout, and user engagement. The results show that in multi-image tweets on the Sina Weibo platform, the image position and layout factors can affect user engagement behavior to a certain extent. That is, (1) for the image position effect, tweets containing 2, 4, 5, and 8 images had the right position effect. Tweets containing 6 and 8 images had the bottom position effect. Tweets containing 3 and 8 images had the edge and middle effects, respectively. Others, in most cases, had symmetric effects. (2) For the image layout effect, Layouts 2, 3, 4, 5, 6, and 8 can positively promote the user engagement potential transfer of images compared with the single image so that the image tweet may achieve user engagement beyond the average level of its potential. However, Layout 7 had a negative effect, and Layout 9 had no significant difference from single-image tweets. The results of this study can provide references for the release of social media image information.
Key wordssocial media visualization    image distribution    user engagement    machine learning    regression analysis
收稿日期: 2021-04-26      出版日期: 2022-01-14
基金资助:国家自然科学基金青年项目(71403201);教育部人文社会科学研究规划基金项目(19YJA870009);陕西省自然科学基础研究计划一般项目(2020JM-056);中央高校基本科研业务费(人文社科)学科交叉项目(SK2021037)
引用本文:   
马晓悦, 孟啸. 用户参与视角下多图推文的图像位置和布局效应[J]. 清华大学学报(自然科学版), 2022, 62(1): 77-87.
MA Xiaoyue, MENG Xiao. Image position and layout effects of multi-image tweets from the perspective of user engagement. Journal of Tsinghua University(Science and Technology), 2022, 62(1): 77-87.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2021.21.036  或          http://jst.tsinghuajournals.com/CN/Y2022/V62/I1/77
  
  
  
  
  
  
  
[1] KEIB K, ESPINA C, LEE Y-I, et al. Picture this:The influence of emotionally valenced images, on attention, selection, and sharing of social media news[J]. Media Psychology, 2018, 21(2):202-221.
[2] STREKALOVA Y A, KRIEGER J L. A picture really is worth a thousand words:Public engagement with the National Cancer Institute on social media[J]. Journal of Cancer Education, 2017, 32(1):155-7.
[3] PARK H, LEE J. Do private and sexual pictures receive more likes on Instagram?[C]//2017 International Conference on Research and Innovation in Information Systems (ICRIIS). Langkawi, Malaysiz:IEEE Press, 2017:1-6.
[4] ARGYRIS Y A, WANG Z, KIM Y, et al. The effects of visual congruence on increasing consumers' brand engagement:An empirical investigation of influencer marketing on instagram using deep-learning algorithms for automatic image classification[J]. Computers in Human Behavior, 2020, 106443.
[5] BRODIE R J, ILIC A, JURIC B, et al. Consumer engagement in a virtual brand community:An exploratory analysis[J]. Journal of Business Research, 2013, 66(1):105-14.
[6] KHAN M L. Social media engagement:What motivates user participation and consumption on YouTube?[J]. Computers in Human Behavior, 2017, 66:236-247.
[7] OELDORF-HIRSCH A, SUNDAR S S. Social and technological motivations for online photo sharing[J]. Journal of Broadcasting & Electronic Media, 2016, 60(4):624-642.
[8] ZAILSKAIT AE·U -JAKŠT AE·U L, OSTREIKA A, JAKŠTAS A, et al. Brand communication in social media:The use of image colours in popular posts[C]//2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). Opatija, Croatia:IEEE Press, 2017:1373-1378.
[9] CHANG Y P, LI Y, YAN J, et al. Getting more likes:The impact of narrative person and brand image on customer-brand interactions[J]. Journal of the Academy of Marketing Science, 2019, 47(6):1027-1045.
[10] PENG Y, JEMMOTT III J B. Feast for the eyes:Effects of food perceptions and computer vision features on food photo popularity[J]. International Journal of Communication, 2018, 12:313-336.
[11] PENG Y L. What makes politicians' Instagram posts popular? Analyzing social media strategies of candidates and office holders with computer vision[J]. The International Journal of Press/Politics, 2021, 26(1):143-166.
[12] LINDELL A K. Left cheek poses garner more likes:The effect of pose orientation on Instagram engagement[J]. Laterality, Brain and Cognition, 2019, 24(5):600-613.
[13] LOWE-CALVERLEY E, GRIEVE R. Thumbs up:A thematic analysis of image-based posting and liking behaviour on social media[J]. Telematics and Informatics, 2018, 35(7):1900-1913.
[14] BELL B T, CASSARLY J A, DUNBAR L. Selfie- objectification:Self-objectification and positive feedback ("likes") are associated with frequency of posting sexually objectifying self-images on social media[J]. Body Image, 2018, 26:83-89.
[15] MANCOSU M, BOBBA G. Using deep-learning algorithms to derive basic characteristics of social media users:The Brexit campaign as a case study[J]. PLoS One, 2019, 14(1):e0211013.
[16] STEINERT-THRELKELD Z C. The future of event data is images[J]. Sociological Methodology, 2019, 49(1):68-75.
[17] KHOSLA A, DAS SARMA A, HAMID R. What makes an image popular?[C]//Proceedings of the 23rd International Conference on World Wide Web. New York:ACM Press, 2014:867-876.
[18] LÜ J N, LIU W, ZHANG M, et al. Multi-feature fusion for predicting social media popularity[C]//Proceedings of the 25th ACM international conference on Multimedia. New York, NY, USA:ACM, 2017:1883-1888.
[19] GELLI F, URICCHIO T, BERTINI M, et al. Image popularity prediction in social media using sentiment and context features[C]//Proceedings of the 23rd ACM International Conference on Multimedia. New York, NY, USA:ACM Press, 2015:907-910.
[20] HIDAYATI S C, CHEN Y L, YANG C L, et al. Popularity meter:An influence-and aesthetics-aware social media popularity predictor[C]//Proceedings of the 25th ACM international conference on Multimedia. New York, NY, USA:ACM Press, 2017:1918-1923.
[21] DING K, WANG R, WANG S. Social media popularity prediction:A multiple feature fusion approach with deep neural networks[C]//Proceedings of the 27th ACM International Conference on Multimedia. New York, NY, USA:ACM Press, 2019:2682-2686.
[22] BENYON D. Designing user experience[M]. Amsterdam:Pearson UK, 2019.
[23] BOLLINI L. Beautiful interfaces. From user experience to user interface design[J]. The Design Journal, 2017, 20(S1):89-101.
[24] BORODITSKY L. Metaphoric structuring:Understanding time through spatial metaphors[J]. Cognition, 2000, 75(1):1-28.
[25] LAKOFF G, JOHNSON M. Metaphors we live by[M]. Chicago, IL, USA:University of Chicago Press, 2008.
[26] MEIER B P, ROBINSON M D. Why the sunny side is up:Associations between affect and vertical position[J]. Psychological Science, 2004, 15(4):243-247.
[27] WEGER U W, PRATT J. Time flies like an arrow:Space-time compatibility effects suggest the use of a mental timeline[J]. Psychonomic Bulletin & Review, 2008, 15(2):426-430.
[28] CHANDON P, HUTCHINSON J W, BRADLOW E T, et al. Does in-store marketing work? Effects of the number and position of shelf facings on brand attention and evaluation at the point of purchase[J]. Journal of Marketing, 2009, 73(6):1-17.
[29] VALENZUELA A, RAGHUBIR P, MITAKAKIS C. Shelf space schemas:Myth or reality?[J]. Journal of Business Research, 2013, 66(7):881-888.
[30] FENKO A, DE VRIES R, VAN ROMPAY T. How strong is your coffee? The influence of visual metaphors and textual claims on consumers' flavor perception and product evaluation[J]. Frontiers in Psychology, 2018, 9:53.
[31] MACHIELS C J A, ORTH U R. Verticality in product labels and shelves as a metaphorical cue to quality[J]. Journal of Retailing and Consumer Services, 2017, 37:195-203.
[32] HURTIENNE J. How cognitive linguistics inspires HCI:Image schemas and image-schematic metaphors[J]. International Journal of Human-Computer Interaction, 2017, 33(1):1-20.
[33] PUNCHOOJIT L, HONGWARITTORRN N. Usability studies on mobile user interface design patterns:A systematic literature review[J]. Advances in Human-Computer Interaction, 2017(16):1-22.
[34] PEARSON R, VAN SCHAIK P. The effect of spatial layout of and link colour in web pages on performance in a visual search task and an interactive search task[J]. International Journal of Human-Computer Studies, 2003, 59(3):327-353.
[35] TODI K, JOKINEN J, LUYTEN K, et al. Individualising graphical layouts with predictive visual search models[J]. ACM Transactions on Interactive Intelligent Systems, 2020, 10(1):1-24.
[36] CHEN S, LI S H, CHEN S M, et al. R-map:A map metaphor for visualizing information reposting process in social media[J]. IEEE Transactions on Visualization and Computer Graphics, 2020, 26(1):1204-1214.
[37] KAHN B E. Using visual design to improve customer perceptions of online assortments[J]. Journal of Retailing, 2017, 93(1):29-42.
[38] VALENTINI C, ROMENTI S, MURTARELLI G, et al. Digital visual engagement:Influencing purchase intentions on Instagram[J]. Journal of Communication Management, 2018, 22(4):362-381.
[39] RZAYEV R, WO Az' niak P W, Dingler T, et al. Reading on smart glasses:The effect of text position, presentation type and walking[C]//Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. New York, NY, USA:ACM, 2018:1-9.
[40] Chen T, Guestrin C. Xgboost:A scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA:ACM, 2016:785-794.
[41] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA:IEEE Press, 2016:770-778.
[42] SUN Y, WANG S H, LI Y K, et al. Ernie:Enhanced represen- tation through knowledge integration[Z/OL].(2019-04-19)[2021-04-01]. https://arxiv.org/abs/1904.09223.
[43] BENESTY J, CHEN J, HUANG Y, et al. Pearson correlation coefficient[M]//Noise reduction in speech processing. Berlin:Springer, Berlin, Heidelberg, 2009:38-40.
[44] OLIVE D J. Multiple linear regression[M]//Linear regression. Switzerland:Springer, Cham, 2017:17-83.
[45] ZHOU D, ZHONG H, DONG W, et al. The metaphoric nature of the ordinal position effect[J]. Quarterly Journal of Experimental Psychology, 2019, 72(8):2121-2129.
[46] MUTLU-BAYRAKTAR D, COSGUN V, ALTAN T. Cognitive load in multimedia learning environments:A systematic review[J]. Computers & Education, 2019, 141:103618.
[1] 吴浩, 牛风雷. 高温球床辐射传热中的机器学习模型[J]. 清华大学学报(自然科学版), 2023, 63(8): 1213-1218.
[2] 代鑫, 黄弘, 汲欣愉, 王巍. 基于机器学习的城市暴雨内涝时空快速预测模型[J]. 清华大学学报(自然科学版), 2023, 63(6): 865-873.
[3] 任建强, 崔亚鹏, 倪顺江. 基于机器学习的新冠肺炎疫情趋势预测方法[J]. 清华大学学报(自然科学版), 2023, 63(6): 1003-1011.
[4] 安健, 陈宇轩, 苏星宇, 周华, 任祝寅. 机器学习在湍流燃烧及发动机中的应用与展望[J]. 清华大学学报(自然科学版), 2023, 63(4): 462-472.
[5] 赵祺铭, 毕可鑫, 邱彤. 基于机器学习的乙烯裂解过程模型比较与集成[J]. 清华大学学报(自然科学版), 2022, 62(9): 1450-1457.
[6] 曹来成, 李运涛, 吴蓉, 郭显, 冯涛. 多密钥隐私保护决策树评估方案[J]. 清华大学学报(自然科学版), 2022, 62(5): 862-870.
[7] 王豪杰, 马子轩, 郑立言, 王元炜, 王飞, 翟季冬. 面向新一代神威超级计算机的高效内存分配器[J]. 清华大学学报(自然科学版), 2022, 62(5): 943-951.
[8] 陆思聪, 李春文. 基于场景与话题的聊天型人机会话系统[J]. 清华大学学报(自然科学版), 2022, 62(5): 952-958.
[9] 李维, 李城龙, 杨家海. As-Stream:一种针对波动数据流的算子智能并行化策略[J]. 清华大学学报(自然科学版), 2022, 62(12): 1851-1863.
[10] 刘强墨, 何旭, 周佰顺, 吴昊霖, 张弛, 秦羽, 沈晓梅, 高小榕. 基于机器学习和瞳孔响应的简易高性能自闭症分类模型[J]. 清华大学学报(自然科学版), 2022, 62(10): 1730-1738.
[11] 汤志立, 王雪, 徐千军. 基于过采样和客观赋权法的岩爆预测[J]. 清华大学学报(自然科学版), 2021, 61(6): 543-555.
[12] 王志国, 章毓晋. 监控视频异常检测:综述[J]. 清华大学学报(自然科学版), 2020, 60(6): 518-529.
[13] 宋宇波, 祁欣妤, 黄强, 胡爱群, 杨俊杰. 基于二阶段多分类的物联网设备识别算法[J]. 清华大学学报(自然科学版), 2020, 60(5): 365-370.
[14] 芦效峰, 蒋方朔, 周箫, 崔宝江, 伊胜伟, 沙晶. 基于API序列特征和统计特征组合的恶意样本检测框架[J]. 清华大学学报(自然科学版), 2018, 58(5): 500-508.
[15] 邹权臣, 张涛, 吴润浦, 马金鑫, 李美聪, 陈晨, 侯长玉. 从自动化到智能化:软件漏洞挖掘技术进展[J]. 清华大学学报(自然科学版), 2018, 58(12): 1079-1094.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《清华大学学报(自然科学版)》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn