[Air-L] Twitter Chatbots

Wed Oct 2 11:36:20 PDT 2019

Thanks everyone for all the helpful suggestions!
I passed them on to him. 

Natalie 

Sent from my iPhone

> On 27 Sep 2019, at 15:40, Shulman, Stu <stu at texifter.com> wrote:
> 
> I would second everything Dr Bolsover just wrote and add this. We are
> currently testing new methods for detecting automation, bots, and trolls in
> the Canadian election. This is a time-sensitive project. If you are fluent
> in Canadian politics, interested in bot detection, and worried about the
> future of democracy, please drop me a line. We have a promising new
> approach, but our goal is to merge many approaches into a more unified
> framework.
> 
> ~stu
> 
> On Fri, Sep 27, 2019 at 3:55 PM Gillian Bolsover <G.Bolsover at leeds.ac.uk>
> wrote:
> 
>> 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
>> Lecturer in Politics and Media<
>> https://essl.leeds.ac.uk/politics/staff/693/dr-gillian-bolsover>
>> University of Leeds
>> 
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>> Latest Article:
>> C. Goron and G. Bolsover. Engagement or control? The impact of the Chinese
>> environmental protection bureaus’ burgeoning online presence in local
>> environmental governance.
>> <https://www.tandfonline.com/doi/full/10.1080/09640568.2019.1628716>
>> <https://www.tandfonline.com/doi/full/10.1080/09640568.2019.1628716>
>> 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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>> 
>> 
>> 
>> --
>> You see before you *Mark Chen, PhD*.
>> Above his head appears a label that changes every time you look at it
>> between "*Hoodie-Wearing Games Scholar Thug*," "*PT Lecturer at UW
>> Bothell*," and
>> "*A very happy young girl looking forward to a bright and wonderful
>> future.*
>> "
>> Do you send him a tweet (*@mcdanger* <http://twitter.com/mcdanger><
>> http://twitter.com/mcdanger>), check
>> out his website (*markdangerchen.net* <http://markdangerchen.net/><
>> http://markdangerchen.net/>), or
>> respond to this email?
>> His desk and surroundings are on fire as he smiles and says, "*everything
>> is fine*."
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>> 
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> 
> 
> 
> -- 
> Dr. Stuart W. Shulman
> Founder and CEO, Texifter
> Cell: 413-992-8513
> LinkedIn: http://www.linkedin.com/in/stuartwshulman
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