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
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-follo... https://journals.sagepub.com/doi/abs/10.1177/0894439317734157?journalCode=ss... mark On Fri, Sep 27, 2019 at 6:54 AM Natalie Rock <drnatalierock@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 _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
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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>), check out his website (*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*."
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@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@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 _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
-- 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>), check out his website (*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*." _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/ listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
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 Support the technologies of the future you want; end-to-end encrypt your emails. PGP Key: 17EC60B3<https://pgp.mit.edu/pks/lookup?op=vindex&search=0x15E8761217EC60B3> 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@u.washington.edu><mailto:markchen@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@gmail.com><mailto:drnatalierock@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 _______________________________________________ The Air-L@listserv.aoir.org<mailto:Air-L@listserv.aoir.org> mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org Join the Association of Internet Researchers: http://www.aoir.org/ -- 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*." _______________________________________________ The Air-L@listserv.aoir.org<mailto:Air-L@listserv.aoir.org> mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/ listinfo.cgi/air-l-aoir.org Join the Association of Internet Researchers: http://www.aoir.org/ _______________________________________________ The Air-L@listserv.aoir.org<mailto:Air-L@listserv.aoir.org> mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org Join the Association of Internet Researchers: http://www.aoir.org/
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@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
Support the technologies of the future you want; end-to-end encrypt your emails. PGP Key: 17EC60B3< https://pgp.mit.edu/pks/lookup?op=vindex&search=0x15E8761217EC60B3>
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@u.washington.edu
<mailto:markchen@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@gmail.com
<mailto:drnatalierock@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 _______________________________________________ The Air-L@listserv.aoir.org<mailto:Air-L@listserv.aoir.org> mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
-- 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*." _______________________________________________ The Air-L@listserv.aoir.org<mailto:Air-L@listserv.aoir.org> mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/ listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
_______________________________________________ The Air-L@listserv.aoir.org<mailto:Air-L@listserv.aoir.org> mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/ _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
-- Dr. Stuart W. Shulman Founder and CEO, Texifter Cell: 413-992-8513 LinkedIn: http://www.linkedin.com/in/stuartwshulman
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@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@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
Support the technologies of the future you want; end-to-end encrypt your emails. PGP Key: 17EC60B3< https://pgp.mit.edu/pks/lookup?op=vindex&search=0x15E8761217EC60B3>
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@u.washington.edu
<mailto:markchen@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@gmail.com
<mailto:drnatalierock@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 _______________________________________________ The Air-L@listserv.aoir.org<mailto:Air-L@listserv.aoir.org> mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
-- 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*." _______________________________________________ The Air-L@listserv.aoir.org<mailto:Air-L@listserv.aoir.org> mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/ listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
_______________________________________________ The Air-L@listserv.aoir.org<mailto:Air-L@listserv.aoir.org> mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/ _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
-- Dr. Stuart W. Shulman Founder and CEO, Texifter Cell: 413-992-8513 LinkedIn: http://www.linkedin.com/in/stuartwshulman _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org
Join the Association of Internet Researchers: http://www.aoir.org/
participants (5)
-
Gillian Bolsover -
Marcela Canavarro -
Mark Chen -
Natalie Rock -
Shulman, Stu