Beyond Contagion: Reality Mining Reveals Complex Patterns of Social Influence
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{"title"=>"Beyond contagion: Reality mining reveals complex patterns of social influence", "type"=>"journal", "authors"=>[{"first_name"=>"Aamena", "last_name"=>"Alshamsi", "scopus_author_id"=>"56703017100"}, {"first_name"=>"Fabio", "last_name"=>"Pianesi", "scopus_author_id"=>"12241486300"}, {"first_name"=>"Bruno", "last_name"=>"Lepri", "scopus_author_id"=>"14008023300"}, {"first_name"=>"Alex", "last_name"=>"Pentland", "scopus_author_id"=>"7102755925"}, {"first_name"=>"Iyad", "last_name"=>"Rahwan", "scopus_author_id"=>"23009881000"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"26313449", "doi"=>"10.1371/journal.pone.0135740", "sgr"=>"84943279939", "arxiv"=>"1507.04192", "scopus"=>"2-s2.0-84943279939", "issn"=>"19326203", "pui"=>"606225644"}, "id"=>"b4627976-1897-3581-a655-de36edff1ed4", "abstract"=>"Contagion, a concept from epidemiology, has long been used to characterize social influence on people’s behavior and affective (emotional) states. While it has revealed many useful insights, it is not clear whether the contagion metaphor is sufficient to fully characterize the complex dynamics of psychological states in a social context. Using wearable sensors that capture daily face-to-face interaction, combined with three daily experience sampling surveys, we collected the most comprehensive data set of personality and emotion dynamics of an entire community of work. From this high-resolution data about actual (rather than self-reported) face-to-face interaction, a complex picture emerges where contagion (that can be seen as adaptation of behavioral responses to the behavior of other people) cannot fully capture the dynamics of transitory states. We found that social influence has two opposing effects on states: adaptation effects that go beyond mere contagion, and complementarity effects whereby individuals’ behaviors tend to complement the behaviors of others. Surprisingly, these effects can exhibit completely different directions depending on the stable personality or emotional dispositions (stable traits) of target individuals. Our findings provide a foundation for richer models of social dynamics, and have implications on organizational engineering and workplace well-being.", "link"=>"http://www.mendeley.com/research/beyond-contagion-reality-mining-reveals-complex-patterns-social-influence-2", "reader_count"=>56, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Researcher"=>8, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>1, "Student > Master"=>10, "Other"=>3, "Student > Bachelor"=>6, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Researcher"=>8, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>13, "Student > Postgraduate"=>1, "Student > Master"=>10, "Other"=>3, "Student > Bachelor"=>6, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>7, "Medicine and Dentistry"=>1, "Agricultural and Biological Sciences"=>2, "Design"=>4, "Neuroscience"=>2, "Arts and Humanities"=>1, "Business, Management and Accounting"=>6, "Physics and Astronomy"=>1, "Psychology"=>12, "Social Sciences"=>7, "Computer Science"=>13}, "reader_count_by_subdiscipline"=>{"Design"=>{"Design"=>4}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>2}, "Social Sciences"=>{"Social Sciences"=>7}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>12}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Computer Science"=>{"Computer Science"=>13}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>6}, "Unspecified"=>{"Unspecified"=>7}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Austria"=>1, "Hungary"=>1, "United States"=>3, "United Kingdom"=>2, "South Africa"=>1, "Germany"=>1, "Spain"=>1}, "group_count"=>1}

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  • {"files"=>["https://ndownloader.figshare.com/files/2230183"], "description"=>"<p><b>(Left) Transition graph for conscientiousness:</b> Nodes represent conscientiousness level of the ego. Arrows represent transitions from one level to another. Transitions are labeled with conditions that affect the corresponding probabilities. Icons represent the conscientiousness levels of alters and ego’s trait level. Symbol ↑ (respectively ↓) indicates an increase (respectively decrease) in transition probability associated with the given combination of alters’ state level and ego’s trait level. For example, if the ego high in the conscientiousness state, then the probability of the ego transitioning to the high level decreases with ego’s contact with alters in the neutral level of the state. Another example is the transition from neutral to low level of conscientiousness, which is moderated by the ego’s trait score. If the ego is high in trait, then the probability of transition from a neutral state to a low state decreases with his contact with alters in high and low levels. But if the ego is low in the trait, then the probability increases instead. <b>(Right) Social influences:</b> The table summarizes the level transition graph by means of adaptation (A) and complementarity (C). Rows represent ego’s state levels; columns are labeled with alters’ state levels and sub-labeled with ego’s trait level (Low or High). Cells report the effects observed when egos in the corresponding state level and trait level interact with alters in the corresponding state level. For example, the square with thick border indicates that when the ego is low in conscientiousness state and also low in conscientiousness trait, contact with alters who are also low in conscientiousness state results in an adaptation effect. Empty cells lack statistically significant effects in a given combination.</p>", "links"=>[], "tags"=>["people", "adaptation effects", "influence", "Social Influence Contagion", "contagion metaphor", "target individuals", "emotion dynamics", "personality", "experience sampling surveys", "Reality Mining", "wearable sensors", "Complex Patterns", "data", "interaction", "complementarity effects"], "article_id"=>1525216, "categories"=>["Uncategorised"], "users"=>["Aamena Alshamsi", "Fabio Pianesi", "Bruno Lepri", "Alex Pentland", "Iyad Rahwan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135740.g002", "stats"=>{"downloads"=>2, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Beyond_Contagion_Reality_Mining_Reveals_Complex_Patterns_of_Social_Influence_Fig_2_/1525216", "title"=>"Beyond Contagion: Reality Mining Reveals Complex Patterns of Social Influence - Fig 2", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-27 02:51:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/2230181"], "description"=>"<p><b>(i) Framework of the Study:</b> First, participants filled a survey capturing personality and affect (stable) traits. Then they filled 3 daily surveys for 30 work days to measure personality and affect (dynamic) states. <b>(ii) Sociometric Badge:</b> Each participant’s social network between any two consecutive surveys was constructed from infrared sensor data from sociometric badges worn around the neck [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135740#pone.0135740.ref049\" target=\"_blank\">49</a>]. <b>(iii) Measuring Situational Factors:</b> An example of how the situational factors (intensity of contacts) is calculated. The ego’s infrared sensor (in the middle) detected 7 alters between two consecutive surveys. Two of the alters were in the high level (green) with 14 infrared hits, leading to intensity of contact 14/2 = 7. Similarly, the intensity of contact with three alters in the neutral level (yellow) is 15/3 = 5 and that for alters in the low level (red) is 20/2 = 10. <b>(iv) Social Influences:</b> Four possible social influences exemplified. Attraction: an ego in the high level interacts with others in the low level, then moves to the low level to adapt to his peers. Repulsion: a participant in the high level interacts with others in the low level, and consequently remains in the high level in complement to his peers. Inertia: a participant in the low level interacts with others in the same level, who prevent him from moving to a different level, maintaining his adaptation to their level. Push: a participant in the low level interacts with others in the same level, as a result pushing him away to a different, complementary level.</p>", "links"=>[], "tags"=>["people", "adaptation effects", "influence", "Social Influence Contagion", "contagion metaphor", "target individuals", "emotion dynamics", "personality", "experience sampling surveys", "Reality Mining", "wearable sensors", "Complex Patterns", "data", "interaction", "complementarity effects"], "article_id"=>1525214, "categories"=>["Uncategorised"], "users"=>["Aamena Alshamsi", "Fabio Pianesi", "Bruno Lepri", "Alex Pentland", "Iyad Rahwan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135740.g001", "stats"=>{"downloads"=>0, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Beyond_Contagion_Reality_Mining_Reveals_Complex_Patterns_of_Social_Influence_Fig_1_/1525214", "title"=>"Beyond Contagion: Reality Mining Reveals Complex Patterns of Social Influence - Fig 1", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-27 02:51:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/2230192", "https://ndownloader.figshare.com/files/2230193"], "description"=>"<div><p>Contagion, a concept from epidemiology, has long been used to characterize social influence on people’s behavior and affective (emotional) states. While it has revealed many useful insights, it is not clear whether the contagion metaphor is sufficient to fully characterize the complex dynamics of psychological states in a social context. Using wearable sensors that capture daily face-to-face interaction, combined with three daily experience sampling surveys, we collected the most comprehensive data set of personality and emotion dynamics of an entire community of work. From this high-resolution data about actual (rather than self-reported) face-to-face interaction, a complex picture emerges where contagion (that can be seen as adaptation of behavioral responses to the behavior of other people) cannot fully capture the dynamics of transitory states. We found that social influence has two opposing effects on states: <i>adaptation</i> effects that go beyond mere contagion, and <i>complementarity</i> effects whereby individuals’ behaviors tend to complement the behaviors of others. Surprisingly, these effects can exhibit completely different directions depending on the stable personality or emotional dispositions (stable traits) of target individuals. Our findings provide a foundation for richer models of social dynamics, and have implications on organizational engineering and workplace well-being.</p></div>", "links"=>[], "tags"=>["people", "adaptation effects", "influence", "Social Influence Contagion", "contagion metaphor", "target individuals", "emotion dynamics", "personality", "experience sampling surveys", "Reality Mining", "wearable sensors", "Complex Patterns", "data", "interaction", "complementarity effects"], "article_id"=>1525225, "categories"=>["Uncategorised"], "users"=>["Aamena Alshamsi", "Fabio Pianesi", "Bruno Lepri", "Alex Pentland", "Iyad Rahwan"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0135740.s001", "https://dx.doi.org/10.1371/journal.pone.0135740.s002"], "stats"=>{"downloads"=>6, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Beyond_Contagion_Reality_Mining_Reveals_Complex_Patterns_of_Social_Influence_/1525225", "title"=>"Beyond Contagion: Reality Mining Reveals Complex Patterns of Social Influence", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-08-27 02:51:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/2230186"], "description"=>"<p>Surveys for personality and affect states and traits.</p>", "links"=>[], "tags"=>["people", "adaptation effects", "influence", "Social Influence Contagion", "contagion metaphor", "target individuals", "emotion dynamics", "personality", "experience sampling surveys", "Reality Mining", "wearable sensors", "Complex Patterns", "data", "interaction", "complementarity effects"], "article_id"=>1525219, "categories"=>["Uncategorised"], "users"=>["Aamena Alshamsi", "Fabio Pianesi", "Bruno Lepri", "Alex Pentland", "Iyad Rahwan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135740.t001", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Surveys_for_personality_and_affect_states_and_traits_/1525219", "title"=>"Surveys for personality and affect states and traits.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-08-27 02:51:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/2230185"], "description"=>"<p>Nodes are participants who filled both surveys. Edge thickness is proportional to contact intensity (IR hits) between surveys. Colors represent state levels (red:low, yellow:medium, green:high) and are shown only for egos 1–4 and their alters. The four types of social influence discussed in the text can be seen: ego 1 moved from the neutral level to the high level in the presence of interaction with an alter in the neutral level (push). Ego 2 remained in the high level in correspondence to intense contact with an alter in the high level (inertia). Ego 3 was in the low level and moved to neutral after intense contact with alters in the high level (attraction). Remarkably, the three egos have low scores in their corresponding traits. Ego 4 remained in the low level after contact with an alter in the high level (repulsion). The represented states are creativity for node 1, extraversion for nodes 2 and 4 and agreeableness for node 3.</p>", "links"=>[], "tags"=>["people", "adaptation effects", "influence", "Social Influence Contagion", "contagion metaphor", "target individuals", "emotion dynamics", "personality", "experience sampling surveys", "Reality Mining", "wearable sensors", "Complex Patterns", "data", "interaction", "complementarity effects"], "article_id"=>1525218, "categories"=>["Uncategorised"], "users"=>["Aamena Alshamsi", "Fabio Pianesi", "Bruno Lepri", "Alex Pentland", "Iyad Rahwan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135740.g003", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Two_consecutive_snapshots_of_1st_to_2nd_survey_in_day_1_with_the_transient_social_network_/1525218", "title"=>"Two consecutive snapshots of 1st to 2nd survey in day 1, with the transient social network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-27 02:51:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/2230188"], "description"=>"<p>The mere effects of social-situational factors (intensity with alters in each level: L, N and H) and corresponding traits of egos (T) are reported in the table, if they are statistically significant. The interaction results between the two effect are reported also (<i>L</i>*<i>T</i>, <i>N</i>*<i>T</i> and <i>H</i>*<i>T</i>), if they are statistically significant. The coefficients of the control variables are reported also: the main effect of the time of the day (period) and the interaction between the time of the day and the trait (period*T). Some reported coefficients are relatively small, therefore we used a threshold of 0.001 to consider them relevant. We focus more on the direction of the effect (increase or decrease in the probability) rather than the actual value of the effect.</p>", "links"=>[], "tags"=>["people", "adaptation effects", "influence", "Social Influence Contagion", "contagion metaphor", "target individuals", "emotion dynamics", "personality", "experience sampling surveys", "Reality Mining", "wearable sensors", "Complex Patterns", "data", "interaction", "complementarity effects"], "article_id"=>1525221, "categories"=>["Uncategorised"], "users"=>["Aamena Alshamsi", "Fabio Pianesi", "Bruno Lepri", "Alex Pentland", "Iyad Rahwan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135740.t003", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_of_Conscientiousness_State_/1525221", "title"=>"Results of Conscientiousness State.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-08-27 02:51:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/2230187"], "description"=>"<p>The maximum, minimum, median and mean number of transitions between levels of states for each transition.</p>", "links"=>[], "tags"=>["people", "adaptation effects", "influence", "Social Influence Contagion", "contagion metaphor", "target individuals", "emotion dynamics", "personality", "experience sampling surveys", "Reality Mining", "wearable sensors", "Complex Patterns", "data", "interaction", "complementarity effects"], "article_id"=>1525220, "categories"=>["Uncategorised"], "users"=>["Aamena Alshamsi", "Fabio Pianesi", "Bruno Lepri", "Alex Pentland", "Iyad Rahwan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135740.t002", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_maximum_minimum_median_and_mean_number_of_transitions_between_levels_of_states_for_each_transition_/1525220", "title"=>"The maximum, minimum, median and mean number of transitions between levels of states for each transition.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-08-27 02:51:20"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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