My name is Candy.

I am a digital media student in Leeds.

Weekly Reflection

Week 2 Reading Reflection

p.173, what author reveals in this paragraph on user`s willingness and feeling in personal information share makes me to reflect on my own experiences. I find it is true that when I forwarded or made comments on my interested posts, I felt happy and want to let people to know it. Furthermore, it made me feel proud once others agreed with me by clicking the heart of“like". I initiatively share my personal data with algorithms and further immersed in the information they keep pushing to me.

Reference

Gillespie, T. 2014. The Relevance of Algorithms. In: Gillespie, T. ed. Media Technologies: Essays on Communication, Materiality, and Society. Cambridge, Massachusetts: The MIT Press, pp. 167-193.

Week 3 Reading Reflection

The article explores the increasingly expanding role of algorithms in contemporary society and emphasizes how algorithms have contributed to the transformation of new social order and power structure in the globalization background. First, Bucher points out that algorithms are becoming the core force in the formation of the global order. For example, algorithmic trading and machine learning technologies make capital flows faster and more automated. This trend has not only accelerated the process of globalization, but also changed the pattern of international financial markets (Bucher, 2018, p.18). In public policy, algorithms are widely used in smart city management and resource allocation, thereby improving the efficiency of urban operations. Bucher also reminds us that the application of these technologies is often at the expense of ignoring social differences and public participation (Bucher, 2018, p.21). This new "data order" is manifested in the centralized control of data and the implicit dominance of the algorithm execution process. Bucher further explores the relationship between algorithms and power, pointing out that algorithms themselves have implicit power characteristics. Especially in the social media and digital advertising industries, algorithms do not simply serve to improve user experience, but more to maximize platform profits (Bucher, 2018, p. 30). By analyzing the application of algorithms at all levels of society, Bucher revealed that algorithms are not just technical tools. The implicit power and interest distribution mechanisms they carry have a profound impact on the global political and economic order and social structure. While the popularization and application of algorithms improve efficiency and accuracy, they also strengthen the power status of a small number of groups that control data and technology, further exacerbating social inequality and political decentralization (Burke, 2018, p. 37).

Reference

Bucher, T. 2018. The Multiplicity of Algorithms. In: If… Then: Algorithmic Power and Politics.

Week 4 Reflection

Kate Crawford pointed out in "The Atlas of AI" that artificial intelligence systems need to organize data in a specific hierarchy when processing complex data sets. This hierarchical classification method helps AI models identify and process features at different levels. For example, in image recognition tasks, data is classified by details such as faces, expressions, and backgrounds to ensure that the model can perform tasks efficiently [Crawford, 2021, pp. 89-122]. This is not only to improve the efficiency of technical processing, but also related to the power and political meaning of data in society. As the basic form of data storage and transmission, binary files have greatly improved the efficiency of data processing at the technical level. However, Crawford mentioned that data in binary format is not without limitations, especially when it comes to complex unstructured data involving emotions and cultural backgrounds, binary processing may ignore these details [Crawford, 2021, pp. 118-120]. Therefore, although the processing of binary data has its technical advantages, it also brings certain challenges in terms of the accessibility and understandability of information, especially in social backgrounds and cultural differences. University measure students' "digital engagement" using StREAM@Leeds. However, D'Ignazio and Klein emphasize that this digital behavior-based measurement method often underestimates students' deep engagement and cognitive progress [D'Ignazio, Klein, 2020, pp. 115-119]. Although these quantitative data can provide some reflections of student behavior, they ignore students' non-digital behaviors (such as independent thinking and social interaction outside the classroom) and the dimension of emotional engagement. In order to more accurately understand students' digital engagement, I think university can collect data through open questionnaires, interviews, etc., which can not only capture students' cognitive activities, but also reflect their emotional and social interaction dimensions [D'Ignazio & Klein, 2020, pp. 121-123]. For example, students' learning narratives and course feedback can serve as supplementary data to help educators gain a deeper understanding of students' learning process, rather than relying solely on quantitative data such as login frequency and number of interactions on the platform.

Reference

Crawford, K. 2021. Data. In: The atlas of AI: power, politics, and the planetary costs of artificial intelligence. New Haven: Yale University Press, pp. 89-122.
D'Ignazio, C. and Klein, L.F. 2020. What Gets Counted Counts. In: Data Feminism. Cambridge: The MIT Press, pp. 97-123.

Week 5 Reflection

From the reading, it is my first time to think about the relationship between color and its impact on data visualization in a different way. Before I thought it was related to aesthetics and harmony, or contrast and highlight. Now I realize that there are some color theories that make us to understand the meaning behind the color. Something interesting for me to learn about (Kennedyand Hill, 2017).

Reference

Kennedy, H. and Hill, R.L. 2017. The Pleasure and Pain of Visualising Data in Times of Data Power. Television and New Media. 18(8), pp. 769-782.

Week 6 Reflection

This article explores how disabled women use technology and culture to ‘hack’ and rebel against body, gender and social norms. Through technological innovation (such as assistive technology, online platforms, etc.), women with disabilities can break the conventional body image and gender expression, and then perform physical ‘hacking’ behavior. This ‘hacking’ behavior is not only physical, but also cultural and social. In this way, women with disabilities can effectively resist and transcend the social structures that limit their abilities, freedom and power.This reminds me of the identity of disabled women in society. They are often marginalized and have difficulty participating in mainstream body discourse. The concept of ‘body hacking’ proposed in the article made me re-examine the meaning of the body. This is not only a technological innovation, but also a challenge to social structure, which gives the body new possibilities and power. In this struggle, the body has become the starting point of change.

Reference

Forlano, L. 2016. Hacking the Feminist Disabled Body. Journal of Peer Production, 8, no pagination.

Week 7 Reflection

This article explores how automated facial analysis technology perpetuates and exacerbates the power structures and social inequalities left over from colonialism within the framework of gender identity and body expression. Automated facial recognition technology, especially in gender identification and classification, often relies on stereotyped gender standards and ignores diverse gender expressions. The "automatic essentialization" mentioned in the article refers to the fact that technology further exacerbates the solidification of gender and race by simplifying complex human identities and expressions into fixed categories. This article made me realize that technology is not just a neutral tool, it is a product deeply influenced by social and cultural biases. This reminds us that when developing and using technology, we must consider its social and cultural impact more carefully, especially in sensitive areas such as gender and race. We should be more inclusive and decentralized in technology design to ensure that the identity diversity of each individual is equally respected and reflected.

Reference

Scheuerman, M. K., Pape, M., & Hanna, A. 2021. Auto-essentialization: Gender in automated facial analysis as extended colonial project. Big Data & Society, 8(2).

Week 8 Reflection

In the first article, Sumpter made a detailed classification of friends. First, he divided friends into two categories: "strong ties" and "weak ties". This classification originated from Granovetter's "weak tie theory", which emphasized that an individual's social network includes both close friends and accidental social connections. Strong-tie friends are those with whom we often interact, communicate deeply, and share private lives, while weak-tie friends are friends with whom we are more distant and usually only keep in touch through social media (Sumpter, D. 2018, p15). Second, Sumpter also mentioned the difference between "online friends" and "offline friends", emphasizing how digital platforms have changed our understanding of friendships. In the context of social media today, many people have a large number of online friends. Although these friends exist in the virtual world, they have less interaction in real life. In addition, he also distinguished between "functional friends" and "emotional friends", that is, friendships established for specific purposes (such as work cooperation) and close relationships developed based on emotional ties and common interests (Sumpter, D. 2018, p16). Although Sumpter's classification system is relatively comprehensive, he does not seem to involve the category of "chance friends". This type of friend may appear in certain specific situations, such as a brief acquaintance during travel, or a connection established by chance in life. Although these friends have low interaction frequency, they can provide emotional support or practical help to individuals at certain times, constituting an important but under-emphasized type of relationship in social networks.

In the second article, Cheney-Lippold proposed the concept of "algorithmic identity" and explored how digital algorithms shape and reconstruct the social identity of individuals. Algorithmic identity is a digital image of an individual constructed by various data collection, analysis and processing processes. Cheney-Lippold emphasized that the identity of an individual is no longer a static, inherent concept, but a dynamic process that is constantly manipulated, predicted and reshaped by algorithms. He pointed out that in today's digital environment, algorithms not only play a core role in predicting user behavior and optimizing advertising recommendations, but also in shaping social groups and personal identity. Algorithms automatically generate "digital" labels and attributes of individuals through the analysis of big data, which will affect the identity performance of individuals in virtual spaces such as social networks and online platforms. In addition, the data on which these algorithms are based are often biased, further exacerbating the stereotyped identity and the solidification of social classification.

Reference

Sumpter, D. 2018. Chapter 3: The Principal Components of Friendship. In: Outnumbered: From Facebook and Google to Fake News and Filter-Bubbles - The Algorithms That Control Our Lives. London: Bloomsbury.
Cheney-Lippold, J. 2017. Introduction. In: We Are Data: Algorithms and The Making of Our Digital Selves. New York: NYU Press, pp. 1-32.

Week 9 Reflection

In the first article emphasizes that the social world is not a static or fixed entity, but a pluralistic field of dynamic, intertwined practices and interactions. The construction of the social world is achieved through the continuous interaction of individual and group behavior, culture, technology and media, and this process is always changing. The definition of the social world is not limited to physical space, but also covers virtual environments and online communities, the boundaries of which are often defined by the use of technology and media. From my personal perspective, I belong to a highly digital and information-based society. My daily life depends on the flow and processing of data in the global information network, and this relationship constitutes my "connection" with the outside world. My social world is digital, and it is based on the interaction between people and technology, rather than direct social or cultural context.

In the second article, the authors emphasize that when customizing ethical methods for online research, researchers need to pay attention to multiple ethical dimensions. First, informed consent is the basis, especially in a digital environment, researchers must ensure that participants fully understand how they will be studied and how data will be collected and used. Second, privacy and anonymity are key issues, especially in research on social media or public platforms, it is crucial to ensure that the identity of participants is not leaked or traced back to their personal lives.

Reference

Pink, S., Horst, H.A., Postill, J., Hjorth, L., Lewis, T. and Tacchi, J. 2016. Chapter 6: Researching Social Worlds. In: Digital ethnography: principles and practice. Los Angeles: SAGE, pp. 101-122.
Tiidenberg, K., 2018. Chapter 30: Ethics in digital research. In: Flick, U. (ed.) The Sage handbook of qualitative data collection. London: SAGE Publications Ltd, pp. 466-479.

Workshop

Data visualization

I collected the data of tourist flow in famous scenic spots of Xi`an, a well-known historical city in China. The data of tourist flow was collected from top 3 scenic spots, Tang Paradise (a theme park) that is coded as 1788, Ancient city wall that is coded as 1991 and Terra-Cotta Warriors that is coded as 3502. There are 4 scenic status: 0 is for close, 1 is for open, 2 and 3 are for special open status remarked in notice.

After cleaning up and classifying by scenic_id, the data was sorted with days and hours respectively. Calculation details as below:

Total tourists by hour = first value of ‘total_tourists’ of next hour – first value of ‘total_tourists’ of current hour

Total tourists by day = add up the number of Total tourists by hour of the day

Average number of tourists by hour = Total tourists by hour / number of hours

Further mark the days as the day of week