[Air-L] Twitter Chatbots
Gillian Bolsover
G.Bolsover at leeds.ac.uk
Fri Sep 27 12:54:36 PDT 2019
Hi Natalie (and others),
As a knee-jerk reaction, I'd say this was too much for a student project unless there was significant prior knowledge and training in relevant quantitative research methods or time to acquire those or an existing dataset in mind that could be worked with. But a few ideas briefly.
1 - A chatbots and bots are not the same thing. I haven't seem much about chatbots used in politics apart from, if I remember correctly, a Le Keqiang WeChat chatbot developed around the 2017 National Congress, which I think was more humorous/cute than influencing.
2 - Bot identification: The easiest thing to do is to plug stuff into the BotorNot (Botometer) api (check out any of the publications by the group who developed this too) but also being aware that people use lots of different methods and no method is perfect. I've seen things as simple as just per day post volume used to decide "likely automation." I always check the post source in metadata (if you are working directly with API derived posts) as this way that produces no false positives but likely lots of false negatives. In the recent stuff over pro-China automation surrounding Hong Kong on Twitter, a sudden switch in language and content from English content about sports to Chinese content about Hong Kong was seen as a predictor. BotorNot is probably sufficient for a student project unless the main point is to develop new means of bot identification.
3 - I'd think carefully about using network analysis to assess the influence of bots. Bots tend to work in networks reposting each others content having mutual friendship connections. Indeed, this is one of the best ways of really identifying them is similarity and similarity in practice across a large group of accounts. Thus, there is the potential for circularity in using network analysis metrics of influence to assess bots that are programmed to work in a networked way. This isn't to say that it isn't a useful analysis but there is an extra level of complication to interpreting the metrics when working with bots as opposed to just human networks because bots work in networks.
Dr Gillian Bolsover
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University of Leeds
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On 27/09/2019 19:57, Marcela Canavarro wrote:
This one is not about bots but it may bring good clues on network strength,
using a few metrics available on gephi:
https://www.tandfonline.com/doi/abs/10.1080/1369118X.2015.1043315
On Friday, September 27, 2019, Mark Chen <markchen at u.washington.edu><mailto:markchen at u.washington.edu> wrote:
Hi,
It's been in the news a bit over the last couple of years so that might be
a good place to start.
https://www.buzzfeed.com/alexspence/nigel-farages-
brexit-party-twitter-following
https://journals.sagepub.com/doi/abs/10.1177/0894439317734157?journalCode=
ssce
mark
On Fri, Sep 27, 2019 at 6:54 AM Natalie Rock <drnatalierock at gmail.com><mailto:drnatalierock at gmail.com>
wrote:
This one is out of my wheelhouse but maybe you know!I have a student who
wants to investigate the impact Twitter chatbots have on framing
political
conversations, driving specific discussions, and determining voter
intentions. His focus is the Brexit referendum.
He wants to first classify tweets as coming from bots and then measure
the
impact of the tweets using three elements: strength of network
connection:
how important the influencing group of people or their status are to you;
Immediacy of network connection: how close the group are to you (physical
distance and time) at the time of the influence attempt; number: How many
people are present in the environment.
I can foresee some challenges but like I say, this type of analysis is
very much outside of my specialism. This is an ambitious student so
before
I recommend an alternative approach: Is there a strategy anyone can
recommend that will allow him to correctly classify tweets as coming from
bots? How can he measure strength and immediacy of network connection?
Can
anyone recommend any publications for him?
Thank y’all!
Natalie
Sent from my iPhone
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