Lecture Colour Key:
2 3 4 5 6 7
Lecture Key Concepts Theory Takeaways
Appel, M., Marker, C., & 3 Well being: Social media use and
Gnambs, T. (2020). Are State of being comfortable, well-being:
social media ruining our healthy or happy — paper - Only weak support for
lives? A review of focuses on well-being in terms negative linear
meta-analytic evidence. of; self-esteem, life satisfaction, association
Review of General loneliness and social capital - No strong link between
Psychology, 24(1), 60–74. intensity of SNS and
http://doi.org/10.1177/108 Narcissism: loneliness, self-esteem,
9268019880891 excessive interest in or life satisfaction or
admiration of oneself and one's self-reported depression
physical appearance - SNSs seem to provide a
platform for the creation
and maintenance of
close and intimate (but
also more shallow)
relationships
- However, it is
questionable whether
increases in social
capital with the help of
SNSs can translate into
meaningful well-being
gains
Social media use and academic
achievement:
- There is no indication for
potential devastating
effects
, Social media use and
narcissism:
- Small to moderate
associations
- Narcissism is liked to
SNS activities
- Tend to have more
social media friends
- Popular activities that
enable self-promotion
are strongly associated
with narcissism
Boyd, d., & Crawford, K. 7 Big data: Big Data introduces a
(2012). Critical questions A cultural, technological, and "computational turn," altering
for big data. Information, scholarly phenomenon that research methods, perspectives
Communication & rests on the interplay of… on knowledge, and reshaping
Society, 15(5), 662–679. - Technology (maximising fundamental questions about
http://doi.org/10.1080/136 computation power and information and reality → Just
9118X.2012.678878 algorithmic accuracy) as economic tools can shape
- Analysis (drawing on the economic realities they
large data sets to measure, Big Data tools shape
identify patterns in order the social realities they analyse
to make claims)
- Mythology (widespread Social scientists have long
belief that large data focused on acknowledging
sets offer a higher form biases and limitations,
of intelligence and recognizing that a dataset’s size
knowledge) doesn’t automatically make it
random or representative →
Fordism: Researchers’ identities and
Ford’s system of mass perspectives influence analysis,
production which revolutionised meaning data must be
manufacturing by replacing approached critically to avoid
,skilled labour with automation skewed interpretations
and standardised processes, → The process of "data
impacting society and labour cleaning" introduces
fundamentally subjectivity, as researchers
decide what data to include or
Apophenia: exclude, which can shape
Seeing patterns where none findings
actually exist, simply because → Big Data may lead to
enormous quantities of data can apophenia
offer connections that radiate in
all directions Big Data and whole data are
also not the same → Without
taking into account the sample
of a data set, the size of the
data set is meaningless
→ When researchers approach
a data set, they need to
understand the limits of which
questions they can ask of a
data set and what
interpretations are appropriate
→ Research insights can be
found at any level, including at
very modest scales (small data)
Large data sets can be
modelled, data are often
reduced to what can fit into a
mathematical model – taken out
of context, data lose meaning
and value
Data are not generic. There is
value to analysing data
, abstractions, yet retaining
context remains critical,
particularly for certain lines of
inquiry → Context is hard to
interpret at scale and even
harder to maintain when data
are reduced to fit into a model
→ Managing context in light of
Big Data will be an ongoing
challenge
Very little is understood about
the ethical implications
underpinning the Big Data
phenomenon.
There are also significant
questions of truth, control, and
power in Big Data studies:
researchers have the tools and
the access, while social media
users as a whole do not
→ Their data were created in
highly context-sensitive spaces,
→ some users would not give
permission for their data to be
used elsewhere
→ many are not aware of the
multiplicity of agents and
algorithms currently gathering
and storing their data for future
use
Limited access to Big Data
