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social media:
the good, the bad and the ugly
Josh Cowls
Oxford Internet Institute
the internet: an Information and
Communications Technology
the internet: an Information and
Communications Technology
the internet: an Information and
Communications Technology
the internet: an Information and
Communications Technology
social media: platforms that facilitate
information and communication over the
internet
social media: platforms that facilitate
information and communication over the
internet
… of different sorts, to varying degrees …
For the collection and diffusion of
information, social media can be a
revolutionary social good
Example: Miracle on the
Hudson
the tweet that ‘made’ Twitter
Example: Flowminder
• Haitian earthquake struck in 2010 -> led to mass migration away
from capital Port Au Prince.
• Flowminder Foundation asked for phone records of Haitians to
show location of calls from 1.9 million SIM cards over 200 days.
Data used to estimate movement of people around the country,
aiding disaster relief.
• These estimates of geographical distribution of people across Haiti
were later shown to match well to estimates from a large
retrospective UNFPA household survey.Bengtsson L, Lu X, Thorson A, Garfield R, von Schreeb J. (2011). Improved Response to Disasters and Outbreaks by Tracking Population
Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti. PLoS Med 88: e1001083.
Example: Ushahidi
• Ushahidi is a nonprofit technology development company which develops
free and open source software for information collection, aggregation and
visualisation.
• Stop Stock-Outs is a campaign to ensure access to essential medicines by
using Ushahidi and Frontline SMS to map the availability of essential
medicines at public health facilities in several African countries. These are
medicines used to treat common diseases such as malaria, pneumonia,
diarrhoea, HIV, TB and diabetes.
• Researchers visit public health institutions countrywide and check on the
availability of a list of 10 essential medicines. These are medicines that
should be readily available in public health facilities.
• The researchers then report on the results through short messaging
Bellagio Big Data Workshop Participants. (2014). “Big data and positive social change in the developing world: A
white paper for practitioners and researchers.” Oxford: Oxford Internet Institute. Available online:
http://www.rockefellerfoundation.org/uploads/files/c220f1f3-2e9a-4fc6-be6c-45d42849b897-big-data-and.pdf .
These examples used social media
technologies (SMS, ‘wiki’-style websites) –
but the data were collected either passively
and tacitly (Flowminder) or in a structured,
formulaic way (Stop Stock-Outs)
When information is more mediated:
1. we can be very anti-social
2. offline hierarchies and inequalities can
replicate
3. behaviour is harder to measure
When information is more mediated:
1. we can be very anti-social
2. offline hierarchies and inequalities can
replicate
3. behaviour is harder to measure
anti-social media
anti-social media
Retweet network of political communication on Twitter.
Red cluster: 93% right-leaning users
Blue cluster: 80% left-leaning users
M. D. Conover, J. Ratkiewicz, M. Francisco, B. Gonc¸alves, A. Flammini, F. Menczer, ‘Political
Polarization on Twitter’, presented at ICWSM 2011
anti-social media
Release of celebrities’ private photos
“I didn't take the money and run. S**t got weird once I
started posting samples. … People wanted s*** for free.
Sure, I got $120 with my bitcoin address, but when you
consider how much time was put into acquiring this stuff
(i'm not the hacker, just a collector), and the money (i
paid a lot via bitcoin as well to get certain sets when this
stuff was being privately traded Friday/Saturday) I really
didn't get close to what I was hoping.”
anti-social media
anti-social media
the core problem: the internet doesn’t
discriminate
When information is more mediated:
1. we can be very anti-social
2. offline hierarchies and inequalities can
replicate
3. behaviour is harder to measure
hierarchical or horizontal?
hierarchical or horizontal?
in theory
hierarchical or horizontal?
in theoryin reality
hierarchical or horizontal?
Kwak et al 2010:
• Low reciprocity: only 22.1% of dyads are
reciprocal
• Offline celebrities are most followed on
Twitter
Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. What
is Twitter, a Social Network or a News Media? Proceedings of the 19th
International World Wide Web (WWW) Conference, April 26-30, 2010,
Example: trolling celebrities
Even the ugliest of behaviour is based on
network effects
Example: Twitter during #PMQs
• Old media elites on new media platforms
• The difference between speaking and
being heard
• Research questions:
– Who were the most-followed users active during
the debates?
– Were they media elites or from outside the
traditional mainstream?
– Were non-mainstream voices rebroadcasted by
elites through the use of retweets or links?
Example: Twitter during PMQs
The mass media still hold
sway,
but with a diluted reach
24/04/13, #PMQs
The mass media seldom incorporated
outside voices
a) By using the retweet function infrequen
0.00%
20.00%
40.00%
60.00%
Media elite Political elite Others
Percentage of tweets that
were retweets
b) By mostly retweeting each othe
Who did media elites retweet?
Other media
elites
Political elites
Others
Example: los indignados
Gonzalez-Bailon, S., Borge-Holthoefer, J. and Moreno, Y. (2013) Broadcasters and Hidden Influentials in
Online Protest Diffusion. American Behavioral Scientist. doi:10.1177/0002764213479371
When information is more mediated:
1. we can be very anti-social
2. offline hierarchies and inequalities can
replicate
3. behaviour is harder to measure
not made to measure
The messier the data, the harder to measure
Example: using social media to
measure public opinion
Cost
Utility
Big data model
Standard sample survey Social media analysis
Example: using social media to
measure public opinion
But challenges remain:
Representativeness
Reliability
Replicability
≠
Example: mapping tweets during
Hurricane Sandy
Shelton, T et al, ‘Mapping the data shadows of Hurricane Sandy: Uncovering the
sociospatial dimensions of ‘big data’. Geoforum Volume 52, March 2014, pps 167-79
Example: indyref on Facebook
“To produce the report, Facebook chose a
basket of phrases that represented the Yes
campaign, and another set that represented
Better Together. Each mention of these
phrases, by anybody using Facebook, counted
towards their respective totals.”
The data makes no distinction between posts
in favour of a particular phrase, and those
which denigrate it. If someone posted "Let's get
behind 'Yes Scotland'", a friend liked it, another
shared it, and a third commented "I agree, I'm
voting Yes", four interactions were added to
Facebook's Yes tally. Equally, a user who
posted "I can't bear 'Yes Scotland'" - with
friends who liked, shared and commented in
agreement - had exactly the same impact on
the statistics.
Facebook also reports that "in personality
politics, Salmond has a decisive victory over
Darling". The leader of the Yes campaign has
prompted 700,000 interactions, whereas the
leader of Better Together has prompted
250,000. Here, the firm compiled the figures in
a similar way, counting the number of times
When information is more mediated:
1. we can be very anti-social
2. offline hierarchies and inequalities can
replicate
3. behaviour is harder to measure
When information is more mediated:
1. we can be very anti-social
2. offline hierarchies and inequalities can
replicate
3. behaviour is harder to measure
…but…
The data remains hugely powerful, for those
who have it
“In God we trust…
“In God we trust…
… all others bring data”
W. Edwards Deming
Example: using Facebook to
influence emotions
700,000 users were exposed to slightly
more negative or slightly more positive
content in their news feed, which changed
their own posting behaviour.
http://www.pnas.org/conte
nt/111/24/8788.full
Example: using Facebook to
encourage people to vote
“A randomized controlled trial of political mobilization
messages delivered to 61 million Facebook users during the
2010 US congressional elections. The results show that the
messages directly influenced political self-expression,
information seeking and real-world voting behaviour of millions
of people. Furthermore, the messages not only influenced the
users who received them but also the users’ friends, and
friends of friends.”
Robert M. Bond, Christopher J. Fariss, Jason J. Jones, Adam D. I. Kramer. Cameron Marlow, Jaime E. Settle & James H. Fowler. A 61-million-
person experiment in social influence and political mobilization Nature 489, 295–298 (13 September 2012) doi:10.1038/nature11421
social media (meta)data +
algorithms =
Example: Chicago’s ‘predictive
policing’
“When the Chicago Police Department sent one of its
commanders to Robert McDaniel’s home last summer, the 22-
year-old high school dropout was surprised. Though he lived in a
neighborhood well-known for bloodshed on its streets, he hadn’t
committed a crime or interacted with a police officer recently. And
he didn’t have a violent criminal record, nor any gun violations.
Yet, there stood the female police commander at his front door
with a stern message: if you commit any crimes, there will be
major consequences. We’re watching you.
What McDaniel didn’t know was that he had been placed on the
city’s “heat list” — an index of the roughly 400 people in the city of
Chicago supposedly most likely to be involved in violent crime.”
Summary
1. For the collection and diffusion of
information, social media can be a
revolutionary social good
2. When information is more mediated:
• we can be very anti-social
• offline hierarchies and inequalities can replicate
• behaviour is harder to measure
3. The data remains hugely powerful, for
those who have it
@JoshCowls

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Social Media: Revolution and Risks

  • 1. social media: the good, the bad and the ugly Josh Cowls Oxford Internet Institute
  • 2. the internet: an Information and Communications Technology
  • 3. the internet: an Information and Communications Technology
  • 4. the internet: an Information and Communications Technology
  • 5. the internet: an Information and Communications Technology
  • 6. social media: platforms that facilitate information and communication over the internet
  • 7. social media: platforms that facilitate information and communication over the internet … of different sorts, to varying degrees …
  • 8. For the collection and diffusion of information, social media can be a revolutionary social good
  • 9. Example: Miracle on the Hudson the tweet that ‘made’ Twitter
  • 10. Example: Flowminder • Haitian earthquake struck in 2010 -> led to mass migration away from capital Port Au Prince. • Flowminder Foundation asked for phone records of Haitians to show location of calls from 1.9 million SIM cards over 200 days. Data used to estimate movement of people around the country, aiding disaster relief. • These estimates of geographical distribution of people across Haiti were later shown to match well to estimates from a large retrospective UNFPA household survey.Bengtsson L, Lu X, Thorson A, Garfield R, von Schreeb J. (2011). Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti. PLoS Med 88: e1001083.
  • 11. Example: Ushahidi • Ushahidi is a nonprofit technology development company which develops free and open source software for information collection, aggregation and visualisation. • Stop Stock-Outs is a campaign to ensure access to essential medicines by using Ushahidi and Frontline SMS to map the availability of essential medicines at public health facilities in several African countries. These are medicines used to treat common diseases such as malaria, pneumonia, diarrhoea, HIV, TB and diabetes. • Researchers visit public health institutions countrywide and check on the availability of a list of 10 essential medicines. These are medicines that should be readily available in public health facilities. • The researchers then report on the results through short messaging Bellagio Big Data Workshop Participants. (2014). “Big data and positive social change in the developing world: A white paper for practitioners and researchers.” Oxford: Oxford Internet Institute. Available online: http://www.rockefellerfoundation.org/uploads/files/c220f1f3-2e9a-4fc6-be6c-45d42849b897-big-data-and.pdf .
  • 12. These examples used social media technologies (SMS, ‘wiki’-style websites) – but the data were collected either passively and tacitly (Flowminder) or in a structured, formulaic way (Stop Stock-Outs)
  • 13. When information is more mediated: 1. we can be very anti-social 2. offline hierarchies and inequalities can replicate 3. behaviour is harder to measure
  • 14. When information is more mediated: 1. we can be very anti-social 2. offline hierarchies and inequalities can replicate 3. behaviour is harder to measure
  • 16. anti-social media Retweet network of political communication on Twitter. Red cluster: 93% right-leaning users Blue cluster: 80% left-leaning users M. D. Conover, J. Ratkiewicz, M. Francisco, B. Gonc¸alves, A. Flammini, F. Menczer, ‘Political Polarization on Twitter’, presented at ICWSM 2011
  • 17. anti-social media Release of celebrities’ private photos “I didn't take the money and run. S**t got weird once I started posting samples. … People wanted s*** for free. Sure, I got $120 with my bitcoin address, but when you consider how much time was put into acquiring this stuff (i'm not the hacker, just a collector), and the money (i paid a lot via bitcoin as well to get certain sets when this stuff was being privately traded Friday/Saturday) I really didn't get close to what I was hoping.”
  • 19. anti-social media the core problem: the internet doesn’t discriminate
  • 20. When information is more mediated: 1. we can be very anti-social 2. offline hierarchies and inequalities can replicate 3. behaviour is harder to measure
  • 23. hierarchical or horizontal? in theoryin reality
  • 24. hierarchical or horizontal? Kwak et al 2010: • Low reciprocity: only 22.1% of dyads are reciprocal • Offline celebrities are most followed on Twitter Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. What is Twitter, a Social Network or a News Media? Proceedings of the 19th International World Wide Web (WWW) Conference, April 26-30, 2010,
  • 25. Example: trolling celebrities Even the ugliest of behaviour is based on network effects
  • 26. Example: Twitter during #PMQs • Old media elites on new media platforms • The difference between speaking and being heard • Research questions: – Who were the most-followed users active during the debates? – Were they media elites or from outside the traditional mainstream? – Were non-mainstream voices rebroadcasted by elites through the use of retweets or links?
  • 27. Example: Twitter during PMQs The mass media still hold sway, but with a diluted reach 24/04/13, #PMQs The mass media seldom incorporated outside voices a) By using the retweet function infrequen 0.00% 20.00% 40.00% 60.00% Media elite Political elite Others Percentage of tweets that were retweets b) By mostly retweeting each othe Who did media elites retweet? Other media elites Political elites Others
  • 28. Example: los indignados Gonzalez-Bailon, S., Borge-Holthoefer, J. and Moreno, Y. (2013) Broadcasters and Hidden Influentials in Online Protest Diffusion. American Behavioral Scientist. doi:10.1177/0002764213479371
  • 29. When information is more mediated: 1. we can be very anti-social 2. offline hierarchies and inequalities can replicate 3. behaviour is harder to measure
  • 30. not made to measure The messier the data, the harder to measure
  • 31. Example: using social media to measure public opinion Cost Utility Big data model Standard sample survey Social media analysis
  • 32. Example: using social media to measure public opinion But challenges remain: Representativeness Reliability Replicability ≠
  • 33. Example: mapping tweets during Hurricane Sandy Shelton, T et al, ‘Mapping the data shadows of Hurricane Sandy: Uncovering the sociospatial dimensions of ‘big data’. Geoforum Volume 52, March 2014, pps 167-79
  • 34. Example: indyref on Facebook “To produce the report, Facebook chose a basket of phrases that represented the Yes campaign, and another set that represented Better Together. Each mention of these phrases, by anybody using Facebook, counted towards their respective totals.” The data makes no distinction between posts in favour of a particular phrase, and those which denigrate it. If someone posted "Let's get behind 'Yes Scotland'", a friend liked it, another shared it, and a third commented "I agree, I'm voting Yes", four interactions were added to Facebook's Yes tally. Equally, a user who posted "I can't bear 'Yes Scotland'" - with friends who liked, shared and commented in agreement - had exactly the same impact on the statistics. Facebook also reports that "in personality politics, Salmond has a decisive victory over Darling". The leader of the Yes campaign has prompted 700,000 interactions, whereas the leader of Better Together has prompted 250,000. Here, the firm compiled the figures in a similar way, counting the number of times
  • 35. When information is more mediated: 1. we can be very anti-social 2. offline hierarchies and inequalities can replicate 3. behaviour is harder to measure
  • 36. When information is more mediated: 1. we can be very anti-social 2. offline hierarchies and inequalities can replicate 3. behaviour is harder to measure …but…
  • 37. The data remains hugely powerful, for those who have it
  • 38. “In God we trust…
  • 39. “In God we trust… … all others bring data” W. Edwards Deming
  • 40. Example: using Facebook to influence emotions 700,000 users were exposed to slightly more negative or slightly more positive content in their news feed, which changed their own posting behaviour. http://www.pnas.org/conte nt/111/24/8788.full
  • 41. Example: using Facebook to encourage people to vote “A randomized controlled trial of political mobilization messages delivered to 61 million Facebook users during the 2010 US congressional elections. The results show that the messages directly influenced political self-expression, information seeking and real-world voting behaviour of millions of people. Furthermore, the messages not only influenced the users who received them but also the users’ friends, and friends of friends.” Robert M. Bond, Christopher J. Fariss, Jason J. Jones, Adam D. I. Kramer. Cameron Marlow, Jaime E. Settle & James H. Fowler. A 61-million- person experiment in social influence and political mobilization Nature 489, 295–298 (13 September 2012) doi:10.1038/nature11421
  • 42.
  • 43. social media (meta)data + algorithms =
  • 44. Example: Chicago’s ‘predictive policing’ “When the Chicago Police Department sent one of its commanders to Robert McDaniel’s home last summer, the 22- year-old high school dropout was surprised. Though he lived in a neighborhood well-known for bloodshed on its streets, he hadn’t committed a crime or interacted with a police officer recently. And he didn’t have a violent criminal record, nor any gun violations. Yet, there stood the female police commander at his front door with a stern message: if you commit any crimes, there will be major consequences. We’re watching you. What McDaniel didn’t know was that he had been placed on the city’s “heat list” — an index of the roughly 400 people in the city of Chicago supposedly most likely to be involved in violent crime.”
  • 45.
  • 46. Summary 1. For the collection and diffusion of information, social media can be a revolutionary social good 2. When information is more mediated: • we can be very anti-social • offline hierarchies and inequalities can replicate • behaviour is harder to measure 3. The data remains hugely powerful, for those who have it